Research Methods, Guidelines and Formats

March 2009

Prof. Dr. Ing.Taffa Tulu P.O.Box 1888 Adama University Adama / Ethiopia Fon +251-221-100053 FAX +251-221-100046 Mobile +251-911-350085 VPResearch@adama-university.net

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Table of Content

Table of Content_______________________________________________________ 2 Research ____________________________________________________________ 5 1. Research processes__________________________________________________ 5

1.1. Scientific Research (Scientific Method) ................................................................ 5 1.2. Historical (Historical Method)................................................................................ 7

2. Research methods ___________________________________________________ 7 2.1. Action research .................................................................................................... 8 2.2. Cartography.......................................................................................................... 8 2.3. Case Study........................................................................................................... 8

2.3.1. Case Selection............................................................................................... 9 2.3.2. Generalizing from Case Studies .................................................................. 10

2.4. Categorization .................................................................................................... 10 2.4.1. The classical view........................................................................................ 11 2.4.2. Conceptual clustering .................................................................................. 11 2.4.3. Prototype Theory ......................................................................................... 12

2.5. Citation Analysis................................................................................................. 12 2.6. Consumer Ethnocentrism................................................................................... 12 2.7. Content Analysis ................................................................................................ 13

2.7.1. Uses of content analysis .............................................................................. 13 2.7.2. The process of a content analysis ............................................................... 13

2.8. Delphi Method .................................................................................................... 14 2.8.1. Key Characteristics...................................................................................... 14

2.9. Ethnography ....................................................................................................... 15 2.10. Experience and Intuition ................................................................................... 16 2.11. Experiment ....................................................................................................... 17

2.11.1. Design of Experiments............................................................................... 17 2.11.2. Controlled Experiments.............................................................................. 18 2.11.3. Natural Experiments .................................................................................. 19 2.11.4. Observational Studies................................................................................ 20 2.11.5. Field Experiments ...................................................................................... 20

2.12. Googling ........................................................................................................... 20 2.13. Interview........................................................................................................... 20 2.14. Mathematical Model ......................................................................................... 21

2.14.1. Building Blocks .......................................................................................... 21 2.14.2. Classifying Mathematical Model ................................................................ 21 2.14.3. Priori Information ....................................................................................... 22 2.14.4. Subjective Information ............................................................................... 23

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2.14.5. Complexity ................................................................................................. 23 2.14.6. Training...................................................................................................... 24 2.14.7. Model Evaluation ....................................................................................... 24 2.14.8. Fit to Empirical Data .................................................................................. 24 2.14.9. Scope of the Model.................................................................................... 24 2.14.10. Philosophical Considerations................................................................... 25

2.15. Participant Observation .................................................................................... 25 2.15.1. Method and Practice.................................................................................. 25 2.15.2. Variations and Related Methods................................................................ 26

2.16. Phenomenology ............................................................................................... 26 2.16.1. Phenomenology in Physical Sciences ....................................................... 26 2.16.2. Phenomenology in Social Statistics ........................................................... 26

2.17. Q Methodology ................................................................................................. 27 2.18. Questionnaire ................................................................................................... 28 2.19. Simulation......................................................................................................... 28

2.19.1. Simulation in Education and Training ........................................................ 28 2.19.2. Clinical Healthcare Simulators ................................................................... 29 2.19.3. Engineering Technology or Process Simulation ........................................ 29

2.20. Statistics ........................................................................................................... 30 2.20.1. Statistical methods..................................................................................... 31 2.20.2. Statistical techniques ................................................................................. 32

2.21. Statistical Surveys ............................................................................................ 32 2.21.1. Structure and standardization .................................................................... 32 2.21.2. Serial Surveys............................................................................................ 33 2.21.3. Modes of Data Collection........................................................................... 33 2.21.4. Sampling.................................................................................................... 33

3. Writing Research Paper ______________________________________________ 34 3.1. Genre ................................................................................................................. 34 3.2. Topic................................................................................................................... 35 3.3. Scope ................................................................................................................. 35 3.4. Thesis................................................................................................................. 36 3.5. Research ............................................................................................................ 36

3.5.1. Understand the types of resources .............................................................. 37 3.5.2. Critically read and evaluate the sources ...................................................... 37 3.5.3. Note-take effectively .................................................................................... 39

3.6. Developing an Outline ........................................................................................ 40 3.7. First Draft............................................................................................................ 41 3.8. Revision.............................................................................................................. 41 3.9. Proofreading....................................................................................................... 43

4. Proposal Writer's Guide ______________________________________________ 44

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4.1. Introduction......................................................................................................... 44 4.2. Parts of a Proposal ............................................................................................. 45

4.2.1. Research Proposals .................................................................................... 45 4.2.2. Proposals for Academic Programs .............................................................. 49

4.3. Inquiries to Private Foundations ......................................................................... 50 4.4. Why Proposals Are Rejected?............................................................................ 51

5. Writing, Approval and Defense Examination of M.Sc and Ph.D Thesis Proposals_ 53 5.1. General Framework of Writing Thesis Proposal ................................................. 53 5. 2. Theses Research Approval Processes.............................................................. 57 5.3. Procedures and Decision Guidelines on M.Sc. Thesis....................................... 58

5.3.1. Procedures .................................................................................................. 58 5.3.2. Decision ....................................................................................................... 59 5.3.3. Graduation ................................................................................................... 61

5.4. Ph.D. Dissertation Defense Examination procedures......................................... 61 5.4.1. Procedure .................................................................................................... 61 5.4.2. Decision ....................................................................................................... 62

5.5. Thesis Advisor’s Remuneration Scheme............................................................ 63 6. Initiation, Submission and Approval of Research Proposals __________________ 64 References __________________________________________________________ 66 Appendix A __________________________________________________________ 68 Appendix B __________________________________________________________ 74 Appendix C__________________________________________________________ 80 Appendix D__________________________________________________________ 81 Appendix E __________________________________________________________ 84

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Research

Research is defined as human activity based on intellectual application in the investigation of matter. Research deals with discovering, interpreting, and the development of methods and systems for the advancement of human knowledge on a wide variety of scientific matters of our world and the universe. It may be classified as scientific and historical research. The scientific research can be sub-divided into basic and applied research. Basic research (also called fundamental or pure research) has as its primary objective the advancement of knowledge and the theoretical understanding of the relations among variables. It is exploratory and often driven by the researcher’s curiosity, interest, and intuition. Therefore, it is sometimes conducted without any practical end in mind, although it may have unexpected results pointing to practical applications. Applied research provides scientific information and theories for the explanation of the nature and the properties of the world around us. It makes practical applications possible. Historical research is embodied in the historical research.

1. Research processes

1.1. Scientific Research (Scientific Method)

Generally, research is understood to follow a certain structural process. Though step order may vary depending on the subject matter and researcher, the following steps are usually part of most formal research, both basic and applied:

Formation of the topic Hypothesis Conceptual definitions Operational definitions Gathering of data Analysis of data Test, revising of hypothesis Conclusion, iteration if necessary

A hypothesis consists either of a suggested explanation for an observable phenomenon or of a reasoned proposal predicting a possible causal correlation among multiple phenomena. The term was derived from the Greek word “hypotithenai” meaning "to put under" or "to suppose." The scientific method requires that one can test a scientific hypothesis. Scientists generally base such hypotheses on previous observations or on extensions of scientific theories. Even though the words "hypothesis" and "theory" are often used synonymously in common and informal usage, a scientific hypothesis is not the same as a scientific theory. A Hypothesis is never to be stated as a question, but always as a statement with an explanation following it. It is not to be a question because it states what he/she thinks or believes will occur. A common misunderstanding is that

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by this method a hypothesis can be proven or tested. Generally a hypothesis is used to make predictions that can be tested by observing the outcome of an experiment. If the outcome is inconsistent with the hypothesis, then the hypothesis is rejected. However, if the outcome is consistent with the hypothesis, the experiment is said to support the hypothesis. This careful language is used because researchers recognize that alternative hypotheses may also be consistent with the observations. In this sense, a hypothesis can never be proven, but rather only supported by surviving rounds of scientific testing and, eventually, becoming widely thought of as true (or better, predictive), but this is not the same as it having been proven. A useful hypothesis allows prediction and within the accuracy of observation of the time, the prediction will be verified. As the accuracy of observation improves with time, the hypothesis may no longer provide an accurate prediction. In this case a new hypothesis will arise to challenge the old, and to the extent that the new hypothesis makes more accurate predictions than the old, the new will supplant it.

A conceptual definition is an element of the scientific research process, in which a specific concept is defined as a measurable occurrence. It basically gives you the meaning of the concept. It is mostly used in fields of philosophy, psychology, communication studies. This is especially important when conducting a content analysis. Examples of ideas that are often conceptually defined include intelligence, knowledge, tolerance, and preference. Following the establishment of a conceptual definition, the researcher must use an operational definition to indicate how the abstract concept will be measured.

Operational Definition is a demonstration of a process — such as a variable, term, or object — relative in terms of the specific process or set of validation tests used to determine its presence and quantity. Properties described in this manner must be sufficiently accessible that persons other than the definer can independently measure or test for them at will. An operational definition is generally designed to model a conceptual definition. The most operational definition is a process for identification of an object by distinguishing it from its background of empirical experience. The binary version produces either the result that the object exists, or that it doesn't, in the experiential field to which it is applied. The classifier version results in discrimination between what is part of the object and what is not part of it. This is also discussed in terms of semantics, pattern recognition, and operational techniques, such as regression. For example, the weight of an object may be operationally defined in terms of the specific steps of putting an object on a weighing scale. The weight is whatever results from following the measurement procedure, which can in principle be repeated by anyone. It is intentionally not defined in terms of some intrinsic or private essence. The operational definition of weight is just the result of what happens when the defined procedure is followed. In other words, what's being defined is how to measure weight for any arbitrary object, and only incidentally the weight of a given object. Operational definitions are also used to define system states in terms of a specific, publicly accessible process of preparation or validation testing, which is repeatable at will. For example, 100 degrees Celsius may be crudely defined by describing the process of

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heating water until it is observed to boil. An item like a brick, or even a photograph of a brick, may be defined in terms of how it can be made. Likewise, iron may be defined in terms of the results of testing or measuring it in particular ways. One simple, every day illustration of an operational definition is defining a cake in terms of how it is prepared and baked (i.e., its recipe is an operational definition). Similarly, the saying, if it walks like a duck and quacks like a duck, it must be some kind of duck, may be regarded as involving a sort of measurement process or set of tests

1.2. Historical (Historical Method)

The historical method comprises the techniques and guidelines by which historians use historical sources and other evidence to research and then to write history. There are various history guidelines commonly used by historians in their work, under the headings of external criticism, internal criticism, and synthesis. This includes higher criticism and textual criticism. Though items may vary depending on the subject matter and researcher, the following concepts are usually part of most formal historical research:

Identification of origin date Evidence of localization Recognition of authorship Analysis of data Identification of integrity Attribution of credibility

2. Research methods

The goal of the research process is to produce new knowledge, which takes three main forms (although, as previously discussed, the boundaries between them may be fuzzy):

Exploratory research, which structures and identifies new problems Constructive research, which develops solutions to a problem Empirical research, which tests the feasibility of a solution using empirical evidence

Research can also fall into two distinct types:

Primary research Secondary research

Primary research (also called field research) involves the collection of data that does not already exist. This can be through numerous forms, including questionnaires and telephone interviews amongst others. This information may be used in such things as questionnaires, magazines, and Interviews. Secondary research (also known as desk research) involves the summary, collation and/or synthesis of existing research rather than primary research, where data is collected from, for example, research subjects or experiments. Research methods used by scholars include:

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1. Action research 2. Cartography 3. Case study 4. Classification 5. Citation Analysis 6. Consumer ethnocentrism and CETSCALE 7. Content or Textual Analysis 8. Delphi method 9. Ethnography 10. Experience and intuition 11. Experiments 12. Googling 13. Interviews 14. Mathematical models 15. Participant observation 16. Phenomenology 17. Q methodology 18. Questionnaires 19. Simulation 20. Statistical analysis 21. Statistical surveys

2.1. Action research

Action research is a reflective process of progressive problem solving led by individuals working with others in teams or as part of a "community of practice" to improve the way they address issues and solve problems. Action research can also be undertaken by larger organizations or institutions, assisted or guided by professional researchers, with the aim of improving their strategies, practices, and knowledge of the environments within which they practice.

2.2. Cartography

Cartography or mapmaking (in Greek chartis = map and graphein = write) is the study and practice of making representations of the Earth on a flat surface. Cartography combines science, aesthetics, and technical ability to create a balanced and readable representation that is capable of communicating information effectively and quickly.

2.3. Case Study

A case study is one of several ways of doing research whether it be social science related or even socially related. Other ways include experiments, surveys, multiple histories, and analysis of archival information. Rather than using samples and following a rigid protocol to examine limited number of variables, case study methods involve an

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in-depth, longitudinal examination of a single instance or event: a case. They provide a systematic way of looking at events, collecting data, analyzing information, and reporting the results. As a result the researcher may gain a sharpened understanding of why the instance happened as it did, and what might become important to look at more extensively in future research. Case studies lend themselves to both generating and testing hypotheses. Another suggestion is that case study should be defined as a research strategy, an empirical inquiry that investigates a phenomenon within its real-life context. Case study research means single and multiple case studies, can include quantitative evidence, relies on multiple sources of evidence and benefits from the prior development of theoretical propositions. Case studies should not be confused with qualitative research and they can be based on any mix of quantitative and qualitative evidence. Single-subject research provides the statistical framework for making inferences from quantitative case-study data.

2.3.1. Case Selection

When selecting a case for a case study, researchers often use information-oriented sampling, as opposed to random sampling. This is because the typical or average case is often not the richest in information. Extreme or atypical cases reveal more information because they activate more basic mechanisms and more actors in the situation studied. In addition, from both an understanding-oriented and an action-oriented perspective, it is often more important to clarify the deeper causes behind a given problem and its consequences than to describe the symptoms of the problem and how frequently they occur. Random samples emphasizing representativeness will seldom be able to produce this kind of insight; it is more appropriate to select some few cases chosen for their validity.

Three types of information-oriented cases may be distinguished:

1. Extreme or deviant cases 2. Critical cases 3. Paradigmatic cases.

Extreme case: The extreme case can be well-suited for getting a point across in an especially dramatic way, which often occurs for well-known case studies.

Critical case: A critical case can be defined as having strategic importance in relation to the general problem. Via this type of strategic sampling, one can save both time and money in researching a given problem.

Paradigmatic case: A Paradigmatic case may be defined as an exemplar or prototype. There exists no predictive theory for how predictive theory comes about. A scientific activity is acknowledged or rejected as good science by how close it is to one or more exemplars; that is, practical prototypes of good scientific work. A paradigmatic case of how scientists do science is precisely such a prototype. It operates as a reference point and may function as a focus for the founding of schools of thought.

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2.3.2. Generalizing from Case Studies

The case study is effective for generalizing using the type of test called falsification, which forms part of critical reflexivity. Falsification is one of the most rigorous tests to which a scientific proposition can be subjected: if just one observation does not fit with the proposition it is considered not valid generally and must therefore be either revised or rejected. For example, the proposition "All swans are white," would be falsified by just one observation of a single black swan. This can have general significance and stimulate further investigations and theory-building. The case study is well suited for identifying "black swans" because of its in-depth approach: what appears to be "white" often turns out on closer examination to be "black."

For instance, Galileo’s rejection of Aristotle’s law of gravity was based on a case study selected by information-oriented sampling and not random sampling. The rejection consisted primarily of a conceptual experiment and later on of a practical one. These experiments, with the benefit of hindsight, are self-evident. Nevertheless, Aristotle’s incorrect view of gravity dominated scientific inquiry for nearly two thousand years before it was falsified. In his experimental thinking, Galileo reasoned as follows: if two objects with the same weight are released from the same height at the same time, they will hit the ground simultaneously, having fallen at the same speed. If the two objects are then stuck together into one, this object will have double the weight and will according to the Aristotelian view therefore fall faster than the two individual objects. This conclusion seemed contradictory to Galileo. The only way to avoid the contradiction was to eliminate weight as a determinant factor for acceleration in free fall. Galileo’s experimentalism did not involve a large random sample of trials of objects falling from a wide range of randomly selected heights under varying wind conditions, and so on. Rather, it was a matter of a single experiment, that is, a case study.

Galileo’s view continued to be subjected to doubt, however, and the Aristotelian view was not finally rejected until half a century later, with the invention of the air pump. The air pump made it possible to conduct the ultimate experiment, known by every pupil, whereby a coin or a piece of lead inside a vacuum tube falls with the same speed as a feather. After this experiment, Aristotle’s view could be maintained no longer. What is especially worth noting, however, is that the matter was settled by an individual case due to the clever choice of the extremes of metal and feather. One might call it a critical case, for if Galileo’s thesis held for these materials, it could be expected to be valid for all or a large range of materials. Random and large samples were at no time part of the picture. However it was Galileo's view that was the subject of doubt as it was not reasonable enough to be Aristotelian view. By selecting cases strategically in this manner one may arrive at case studies that allow generalization.

2.4. Categorization

Categorization is the process in which ideas and objects are recognized, differentiated and understood. Categorization implies that objects are grouped into categories, usually

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for some specific purpose. Ideally, a category illuminates a relationship between the subjects and objects of knowledge. Categorization is fundamental in language, prediction, inference, decision making and in all kinds of environmental interaction. There are many categorization theories and techniques. In a broader historical view, however, three general approaches to categorization may be identified: 1. Classical categorization 2. Conceptual clustering 3. Prototype theory

2.4.1. The classical view

Classical categorization comes to us first from Plato, who, in his Statesman dialogue, introduces the approach of grouping objects based in their similar properties. This approach was further explored and systematized by Aristotle in his Categories treatise, where he analyzes the differences between classes and objects. Aristotle also applied intensively the classical categorization scheme in his approach to the classification of living beings (which uses the technique of applying successive narrowing questions such as "Is it an animal or vegetable?", "How many feet does it have?", "Does it have fur or feathers?", "Can it fly). The classical Aristotelian view claims that categories are discrete entities characterized by a set of properties which are shared by their members. In analytic philosophy, these properties are assumed to establish the conditions which are both necessary and sufficient to capture meaning.

According to the classical view, categories should be clearly defined, mutually exclusive and collectively exhaustive. This way, any entity of the given classification universe belongs unequivocally to one, and only one, of the proposed categories.

2.4.2. Conceptual clustering

Conceptual clustering is a modern variation of the classical approach, and derives from attempts to explain how knowledge is represented. In this approach, classes (clusters or entities) are generated by first formulating their conceptual descriptions and then classifying the entities according to the descriptions. Conceptual clustering developed mainly during the 1980s, as a machine paradigm for unsupervised learning. It is distinguished from ordinary data clustering by generating a concept description for each generated category. Categorization tasks in which category labels are provided to the learner for certain objects are referred to as supervised classification, supervised learning, or concept learning. Categorization tasks in which no labels are supplied are referred to as unsupervised classification, unsupervised learning, or data clustering. The task of supervised classification involves extracting information from the labeled examples that allows accurate prediction of class labels of future examples. This may involve the abstraction of a rule or concept relating observed object features to category labels, or it may not involve abstraction (e.g., exemplar models). The task of clustering involves recognizing inherent structure in a data set and grouping objects together by

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similarity into classes. It is thus a process of generating a classification structure. Conceptual clustering is closely related to fuzzy set theory, in which objects may belong to one or more groups, in varying degrees of fitness.

2.4.3. Prototype Theory

Categorization can be viewed as the process of grouping things based on prototypes. It has also been suggested that categorization based on prototypes is the basis for human development, and that this learning relies on learning about the world via embodiment. A cognitive approach accepts that natural categories are graded (they tend to be fuzzy at their boundaries) and inconsistent in the status of their constituent members. Systems of categories are not objectively "out there" in the world but are rooted in people's experience. Conceptual categories are not identical for different cultures, or indeed, for every individual in the same culture. Categories form part of a hierarchical structure when applied to such subjects as taxonomy in biological classification: higher level: life-form level, middle level: generic or genus level, and lower level: the species level. These can be distinguished by certain traits that put an item in its distinctive category. But even these can be arbitrary and are subject to revision. Categories at the middle level are perceptually and conceptually the more salient. The generic level of a category tends to elicit the most responses and richest images and seems to be the psychologically basic level. Typical taxonomies in zoology for example exhibit categorization at the embodied level, with similarities leading to formulation of "higher" categories, and differences leading to differentiation within categories.

2.5. Citation Analysis

Citation analysis is the examination of the frequency, patterns and graphs of citations in articles and books. It uses citations in scholarly works to establish links to other works or other researchers. It is one of the most widely used methods of bibliometrics. Automated citation analysis has changed the nature of the research allowing millions of citations to be analyzed for large scale patterns. Co-citation coupling and bibliographic coupling are specific kinds of citation analysis.

2.6. Consumer Ethnocentrism

Consumer ethnocentrism is derived from the more general psychological concept of ethnocentrism. Basically, ethnocentric individuals tend to view their group as superior to others. As such, they view other groups from the perspective of their own, and reject those which are different while accepting those which are similar. This in turn is derived from earlier sociological theories of in-groups and out-groups. Consumer ethnocentrism specifically refers to ethnocentric views held by consumers in one country, the in-group, towards products from another country, the out-group. Consumers may believe that it is not appropriate, and possibly even immoral, to buy products from other countries. Purchasing foreign products may be viewed as improper because it costs domestic jobs

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and hurts the economy. The purchase of foreign products may even be seen as simply unpatriotic.

2.7. Content Analysis

Content analysis is a methodology in the social sciences for studying the content of communication. It is most commonly used by researchers in the social sciences to analyze recorded transcripts of interviews with participants. Content analysis is also considered a scholarly methodology in the humanities by which texts are studied as to authorship, authenticity, of meaning. This latter subject includes philology, hermeneutics, and semiotics.

2.7.1. Uses of content analysis

There are three major uses of content analysis: 1. make inferences about the antecedents of a communication 2. describe and make inferences about characteristics of a communication 3. make inferences about the effects of a communication.

2.7.2. The process of a content analysis

Six questions must be addressed in every content analysis: Which data are analyzed? How are they defined? What is the population from which they are drawn? What is the context relative to which the data are analyzed? What are the boundaries of the analysis? What is the target of the inferences? The assumption is that words and phrases mentioned most often are those reflecting important concerns in every communication. Therefore, quantitative content analysis starts with word frequencies, space measurements (column centimeters/inches in the case of newspapers), time counts (for radio and television time) and keyword frequencies. However, content analysis extends far beyond plain word counts, e.g. with keyword in context routines words can be analyzed in their specific context to be disambiguated. Synonyms and homonyms can be isolated in accordance to linguistic properties of a language. Qualitatively, content analysis can involve any kind of analysis where communication content (speech, written text, interviews, images ...) is categorized and classified. In its beginnings, using the first newspapers at the end of 19th century, analysis was done manually by measuring the number of lines and amount of space given a subject. With the rise of common computing facilities like PCs, computer-based methods of analysis are growing in popularity. Answers to open ended questions, newspaper articles, political party manifestoes, medical records or systematic observations in experiments can all be subject to systematic analysis of

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textual data. By having contents of communication available in form of machine readable texts, the input is analyzed for frequencies and coded into categories for building up inferences.

2.8. Delphi Method

The name "Delphi" was derived from the Oracle of Delphi. The Delphi method was developed at the beginning of the cold war to forecast the impact of technology on warfare. The Delphi method is a systematic, interactive forecasting method which relies on a panel of independent experts. The carefully selected experts answer questionnaires in two or more rounds. After each round, a facilitator provides an anonymous summary of the experts’ forecasts from the previous round as well as the reasons they provided for their judgments. Thus, participants are encouraged to revise their earlier answers in light of the replies of other members of the group. It is believed that during this process the range of the answers will decrease and the group will converge towards the "correct" answer. Finally, the process is stopped after a pre-defined stop criterion (e.g. number of rounds, achievement of consensus, stability of results) and the mean or median scores of the final rounds determine the results. Delphi is based on the principle that forecasts from a structured group of experts are more accurate than those from unstructured groups or individuals. Delphi has been widely used for business forecasting and has certain advantages over another structured forecasting approach, prediction markets.

2.8.1. Key Characteristics

The following key characteristics of the Delphi method help the participants to focus on the issues at hand and separate Delphi from other methodologies:

Structuring of information flow: The initial contributions from the experts are collected in the form of answers to questionnaires and their comments to these answers. The panel director controls the interactions among the participants by processing the information and filtering out irrelevant content. This avoids the negative effects of face-to-face panel discussions and solves the usual problems of group dynamics.

Regular feedback: Participants comment on their own forecasts, the responses of others and on the progress of the panel as a whole. At any moment they can revise their earlier statements. While in regular group meetings participants tend to stick to previously stated opinions and often conform too much to group leader, the Delphi method prevents it.

Anonymity of the participants: Usually all participants maintain anonymity. Their identity is not revealed even after the completion of the final report. This stops them from dominating others in the process using their authority or personality, frees them to some extent from their personal biases, minimizes the "bandwagon effect" or "halo

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effect", allows them to freely express their opinions, encourages open critique and admitting errors by revising earlier judgments.

Role of the facilitator: The person coordinating the Delphi method can be known as a facilitator, and facilitates the responses of their panel of experts, who are selected for a reason, usually that they hold knowledge on an opinion or view. The facilitator sends out questionnaires, surveys etc. and if the panel of experts accept, they follow instructions and present their views. Responses are collected and analyzed, then common and conflicting viewpoints are identified. If consensus is not reached, the process continues through thesis and antithesis, to gradually work towards synthesis, and building consensus.

Use in forecasting: First applications of the Delphi method were in the field of science and technology forecasting. The objective of the method was to combine expert opinions on likelihood and expected development time, of the particular technology, in a single indicator. Later the Delphi method was applied in other areas, especially those related to public policy issues, such as economic trends, health and education. It was also applied successfully and with high accuracy in business forecasting.

2.9. Ethnography

Ethnography (Greek ἔθνος ethnos = people and γράφειν graphein = writing) is a genre of writing that uses fieldwork to provide a descriptive study of human societies. Ethnography presents the results of a holistic research method founded on the idea that a system's properties cannot necessarily be accurately understood independently of each other. The genre has both formal and historical connections to travel writing and colonial office reports. Several academic traditions, in particular the constructivist and relativist paradigms, employ ethnographic research as a crucial research method. Many cultural anthropologists consider ethnography the essence of the discipline.

1. Cultural and social anthropology: Cultural anthropology and social anthropology were developed around ethnographic research and their canonical texts which are mostly ethnographies. Cultural & social anthropologists today place such a high value on actually doing ethnographic research that ethnology—the comparative synthesis of ethnographic information—is rarely the foundation for a career. Within cultural anthropology, there are several sub-genres of ethnography. Beginning in the late 1950s and early 1960s, anthropologists began writing "bi-confessional" ethnographies that intentionally exposed the nature of ethnographic research. Cultural anthropologists study and interpret cultural diversity through ethnography based on field work. It provides an account of a particular culture, society, or community. The fieldwork usually involves spending a year or more in another society, living with the local people and learning about their ways of life.

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Ethnographers are participant observers. They take part in events they study because it helps with understanding local behavior and thought.

2. Other Related Fields: Psychology, economics, sociology, geography and cultural studies also produce ethnography. Education, Ethnomusicology, Performance Studies, Folklore, and Linguistics are others fields which have made extensive use of ethnography.

3. Design Ethnography: Anthropologists have used ethnographic data to answer academic questions about consumers and consumption. Businesses, too, have found ethnographers helpful for understanding how people use products and services, as indicated in the increasing use of ethnographic methods to understand consumers and consumption, or for new product development (such as video ethnography). Ethnographers' systematic and holistic approach to real-life experience is valued by product developers, who use the method to understand unstated desires or cultural practices that surround products. Where focus groups fail to inform marketers about what people really do, ethnography links what people say to what they actually do—avoiding the pitfalls that come from relying only on self-reported, focus-group data.

4. Techniques:

Direct, first-hand observation of daily behavior: This can include participant observation.

Conversation with different levels of formality: This can involve small talk to long interviews.

The genealogical method: This is a set of procedures by which ethnographers discover and record connections of kinship, descent and marriage using diagrams and symbols.

Detailed work with key consultants about particular areas of community life In-depth interviewing Discovery of local beliefs and perceptions Problem-oriented research Longitudinal research: This is continuous long-term study of an area or site. Team research Case studies

Not all of these techniques are used by ethnographers, but interviews and participant observation are the most widely used.

2.10. Experience and Intuition

Experience as a general concept comprises knowledge of or skill in or observation of some thing or some event gained through involvement in or exposure to that thing or

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event. The concept of experience generally refers to know-how or procedural knowledge, rather than propositional knowledge.

2.11. Experiment

In scientific inquiry, an experiment (Latin: ex- periri, "to try out") is a method of investigating particular types of research questions or solving particular types of problems. The experiment is a cornerstone in the empirical approach to acquiring deeper knowledge about the world and is used in both natural sciences as well as in social sciences. An experiment is defined, in science, as a method of investigating less known fields, solving practical problems and proving theoretical assumptions.

2.11.1. Design of Experiments

An experiment can be thought of as a specific type of method used in scientific inquiries, and personal questioning, usually to study causality, series of activities using some materials or variables to find an answer to a question. Often the objective is to test a hypothesis: i.e. a tentative explanation of a phenomenon or mechanism of causality. The essence of an experiment is to introduce a change in a system (the independent variable) and to study the effect of this change (the dependent variable). Two fundamental considerations of experimental design are:

That the independent variable is the only factor that varies systematically in the experiment; in other words, that the experiment is appropriately controlled - that confounding variables are eliminated; and

That the dependent variable truly reflects the phenomenon under study (a question of validity) and that the variable can be measured accurately (i.e., that various types of experimental error, such as measurement error can be eliminated).

In a very strict application of the experimental method, hypotheses are tested by critical experiments: ones that can falsify the hypothesis in the case of a non-result (i.e., an experiment showing that the independent variable did not affect the dependent variable as predicted). Such pure applications are rare, however, in part because a result can sometimes be challenged on the basis that an experiment was not sufficiently controlled, that the dependent variable was not valid, or that various forms of error compromised the experiment. The scientific method, as a result, builds in the need for reproducibility (usually termed "replication") and convergent evidence (see also: external validity). The design of experiments attempts to balance the requirements and limitations of the field of science in which one works so that the experiment can provide the best conclusion about the hypothesis being tested. In some sciences, such as physics and chemistry, it is relatively easy to meet the requirements that all measurements be made objectively, and that all conditions can be kept controlled across experimental trials. On the other hand, in other cases such as biology, and medicine, it is often hard to ensure that the conditions of an experiment are performed

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consistently; and in the social sciences, it may even be difficult to determine a method for measuring the outcomes of an experiment in an objective manner.

For this reason, sciences such as physics and several other fields of natural science are sometimes informally referred to as "hard sciences", while social sciences are sometimes informally referred to as "soft sciences"; in an attempt to capture the idea that objective measurements are often far easier in the former, and far more difficult in the latter. In addition, in the social sciences, the requirement for a "controlled situation" may actually work against the utility of the hypothesis in a more general situation. When the desire is to test a hypothesis that works "in general", an experiment may have a great deal of "internal validity", in the sense that it is valid in a highly controlled situation, while at the same time lack "external validity" when the results of the experiment are applied to a real world situation. One of the reasons why this may happen is the Hawthorne effect; another is that partial equilibrium effects may not persist in general equilibrium.

As a result of these considerations, experimental design in the "hard" sciences tends to focus on the elimination of extraneous effects, while experimental design in the "soft" sciences focuses more on the problems of external validity, often through the use of statistical methods. Occasionally events occur naturally from which scientific evidence can be drawn, which is the basis for natural experiments. In such cases the problem of the scientist is to evaluate the natural "design".

2.11.2. Controlled Experiments

A controlled experiment generally compares the results obtained from an experimental sample against a control sample, which is practically identical to the experimental sample except for the one aspect whose effect is being tested. In many laboratory experiments it is good practice to have several replicate samples for the test being performed and have both a positive control and a negative control. The results from replicate samples can often be averaged, or if one of the replicates is obviously inconsistent with the results from the other samples, it can be discarded as being the result of an experimental error (some step of the test procedure may have been mistakenly omitted for that sample). Most often, tests are done in duplicate or triplicate. A positive control is a procedure that is very similar to the actual experimental test but which is known from previous experience to give a positive result. A negative control is known to give a negative result. The positive control confirms that the basic conditions of the experiment were able to produce a positive result, even if none of the actual experimental samples produce a positive result. The negative control demonstrates the base-line result obtained when a test does not produce a measurable positive result; often the value of the negative control is treated as a "background" value to be subtracted from the test sample results. Sometimes the positive control takes the quadrant of a standard curve.

Controlled experiments can be performed when it is difficult to exactly control all the conditions in an experiment. In this case, the experiment begins by creating two or more

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sample groups that are probabilistically equivalent, which means that measurements of traits should be similar among the groups and that the groups should respond in the same manner if given the same treatment. This equivalency is determined by statistical methods that take into account the amount of variation between individuals and the number of individuals in each group. In fields such as microbiology and chemistry, where there is very little variation between individuals and the group size is easily in the millions, these statistical methods are often bypassed and simply splitting a solution into equal parts is assumed to produce identical sample groups. Once equivalent groups have been formed, the experimenter tries to treat them identically except for the one variable that he or she wishes to isolate. Human experimentation requires special safeguards against outside variables such as the placebo effect. Such experiments are generally double blind, meaning that neither the volunteer nor the researcher knows which individuals are in the control group or the experimental group until after all of the data has been collected. This ensures that any effects on the volunteer are due to the treatment itself and are not a response to the knowledge that he is being treated.

In human experiments, a subject (person) may be given a stimulus to which he or she should respond. The goal of the experiment is to measure the response to a given stimulus by a test method.

2.11.3. Natural Experiments

The term "experiment" usually implies a controlled experiment, but sometimes controlled experiments are prohibitively difficult or impossible. In this case researchers resort to natural experiments, also called quasi-experiments. Natural experiments rely solely on observations of the variables of the system under study, rather than manipulation of just one or a few variables as occurs in controlled experiments. To the degree possible, they attempt to collect data for the system in such a way that contribution from all variables can be determined, and where the effects of variation in certain variables remain approximately constant so that the effects of other variables can be discerned. The degree to which this is possible depends on the observed correlation between explanatory variables in the observed data. When these variables are not well correlated, natural experiments can approach the power of controlled experiments. Usually, however, there is some correlation between these variables, which reduces the reliability of natural experiments relative to what could be concluded if a controlled experiment were performed. Also, because natural experiments usually take place in uncontrolled environments, variables from undetected sources are neither measured nor held constant, and these may produce illusory correlations in variables under study.

Much research in several important science disciplines, including economics, political science, geology, paleontology, ecology, meteorology, and astronomy, relies on quasi-experiments. For example, in astronomy it is clearly impossible, when testing the hypothesis "suns are collapsed clouds of hydrogen", to start out with a giant cloud of hydrogen, and then perform the experiment of waiting a few billion years for it to form a sun. However, by observing various clouds of hydrogen in various states of collapse,

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and other implications of the hypothesis (for example, the presence of various spectral emissions from the light of stars), we can collect data we require to support the hypothesis. An early example of this type of experiment was the first verification in the 1600s that light does not travel from place to place instantaneously, but instead has a measurable speed. Observation of the appearance of the moons of Jupiter were slightly delayed when Jupiter was farther from Earth, as opposed to when Jupiter was closer to Earth; and this phenomenon was used to demonstrate that the difference in the time of appearance of the moons was consistent with a measurable of speed.

2.11.4. Observational Studies

Observational studies are very much like controlled experiments except that they lack probabilistic equivalency between groups. These types of experiments often arise in the area of medicine where, for ethical reasons, it is not possible to create a truly controlled group. For example, one would not want to deny all forms of treatment for a life-threatening disease from one group of patients to evaluate the effectiveness of another treatment on a different group of patients. The results of observational studies are considered much less convincing than those of designed experiments, as they are much more prone to selection bias. Researchers attempt to compensate for this with complicated statistical methods such as propensity score matching methods.

2.11.5. Field Experiments

Field experiments are so named in order to draw a contrast with laboratory experiments. Often used in the social sciences, and especially in economic analyses of education and health interventions, field experiments have the advantage that outcomes are observed in a natural setting rather than in a contrived laboratory environment. However, like natural experiments, field experiments suffer from the possibility of contamination: experimental conditions can be controlled with more precision and certainty in the lab.

2.12. Googling

Googling refers to using the Google search engine to obtain information on the Web.

2.13. Interview

An interview is a conversation between two or more people (the interviewer and the interviewee) where questions are asked by the interviewer to obtain information from the interviewee.

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2.14. Mathematical Model

A mathematical model uses mathematical language to describe a system. Mathematical models are used not only in the natural sciences and engineering disciplines (such as physics, biology, earth science, meteorology, and electrical engineering) but also in the social sciences (such as economics, psychology, sociology and political science). A mathematical model usually describes a system by a set of variables and a set of equations that establish relationships between the variables. The values of the variables can be practically anything; real or integer numbers, boolean values or strings. The variables represent some properties of the system, for example, measured system outputs often in the form of signals, timing data, counters, and event occurrence (yes/no). The actual model is the set of functions that describe the relations between the different variables.

2.14.1. Building Blocks

There are six basic groups of variables: decision variables, input variables, state variables, exogenous variables, random variables, and output variables. Since there can be many variables of each type, the variables are generally represented by vectors. Decision variables are sometimes known as independent variables. Exogenous variables are sometimes known as parameters or constants. The variables are not independent of each other as the state variables are dependent on the decision, input, random, and exogenous variables. Furthermore, the output variables are dependent on the state of the system (represented by the state variables). Objectives and constraints of the system and its users can be represented as functions of the output variables or state variables. The objective functions will depend on the perspective of the model's user. Depending on the context, an objective function is also known as an index of performance, as it is some measure of interest to the user. Although there is no limit to the number of objective functions and constraints a model can have, using or optimizing the model becomes more involved (computationally).

2.14.2. Classifying Mathematical Model

Many mathematical models can be classified in some of the following ways:

Linear vs. nonlinear: Mathematical models are usually composed by variables, which are abstractions of quantities of interest in the described systems, and operators that act on these variables, which can be algebraic operators, functions, differential operators, etc. If all the operators in a mathematical model present linearity, the resulting mathematical model is defined as linear. A model is considered to be nonlinear otherwise. The question of linearity and nonlinearity is dependent on context, and linear models may have nonlinear expressions in them. For example, in a statistical linear model, it is assumed that a relationship is linear in the parameters, but it may be

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nonlinear in the predictor variables. Similarly, a differential equation is said to be linear if it can be written with linear differential operators, but it can still have nonlinear expressions in it. In a mathematical programming model, if the objective functions and constraints are represented entirely by linear equations, then the model is regarded as a linear model. If one or more of the objective functions or constraints are represented with a nonlinear equation, then the model is known as a nonlinear model. Nonlinearity, even in fairly simple systems, is often associated with phenomena such as chaos and irreversibility. Although there are exceptions, nonlinear systems and models tend to be more difficult to study than linear ones. A common approach to nonlinear problems is linearization, but this can be problematic if one is trying to study aspects such as irreversibility, which are strongly tied to nonlinearity.

Deterministic vs. probabilistic (stochastic): A deterministic model is one in which every set of variable states is uniquely determined by parameters in the model and by sets of previous states of these variables. Therefore, deterministic models perform the same way for a given set of initial conditions. Conversely, in a stochastic model, randomness is present, and variable states are not described by unique values, but rather by probability distributions.

Static vs. dynamic: A static model does not account for the element of time, while a dynamic model does. Dynamic models typically are represented with difference equations or differential equations.

Lumped vs. distributed parameters: If the model is homogeneous (consistent state throughout the entire system) the parameters are lumped. If the model is heterogeneous (varying state within the system), then the parameters are distributed. Distributed parameters are typically represented with partial differential equations.

2.14.3. Priori Information

Mathematical modeling problems are often classified into black box or white box models, according to how much a priori information is available of the system. A black-box model is a system of which there is no a priori information available. A white-box model (also called glass box or clear box) is a system where all necessary information is available. Practically all systems are somewhere between the black-box and white-box models, so this concept only works as an intuitive guide for approach. Usually it is preferable to use as much a priori information as possible to make the model more accurate. Therefore the white-box models are usually considered easier, because if you have used the information correctly, then the model will behave correctly. Often the a priori information comes in forms of knowing the type of functions relating different variables. For example, if we make a model of how a medicine works in a human system, we know that usually the amount of medicine in the blood is an exponentially decaying function. But we are still left with several unknown parameters; how rapidly does the medicine amount decay, and what is the initial amount of medicine in blood? This example is therefore not a completely white-box model. These parameters have to be estimated through some means before one can use the model.

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In black-box models one tries to estimate both the functional form of relations between variables and the numerical parameters in those functions. Using a priori information we could end up, for example, with a set of functions that probably could describe the system adequately. If there is no a priori information we would try to use functions as general as possible to cover all different models. An often used approach for black-box models are neural networks which usually do not make assumptions about incoming data. The problem with using a large set of functions to describe a system is that estimating the parameters becomes increasingly difficult when the amount of parameters (and different types of functions) increases.

2.14.4. Subjective Information

Sometimes it is useful to incorporate subjective information into a mathematical model. This can be done based on intuition, experience, or expert opinion, or based on convenience of mathematical form. Bayesian statistics provides a theoretical framework for incorporating such subjectivity into a rigorous analysis: one specifies a prior probability distribution (which can be subjective) and then updates this distribution based on empirical data. An example of when such approach would be necessary is a situation in which an experimenter bends a coin slightly and tosses it once, recording whether it comes up heads, and is then given the task of predicting the probability that the next flip comes up heads. After bending the coin, the true probability that the coin will come up heads is unknown, so the experimenter would need to make an arbitrary decision (perhaps by looking at the shape of the coin) about what prior distribution to use. Incorporation of the subjective information is necessary in this case to get an accurate prediction of the probability, since otherwise one would guess 1 or 0 as the probability of the next flip being heads, which would be almost certainly wrong.

2.14.5. Complexity

In general, model complexity involves a trade-off between simplicity and accuracy of the model. While added complexity usually improves the fit of a model, it can make the model difficult to understand and work with, and can also pose computational problems, including numerical instability. For example, when modeling the flight of an aircraft, we could embed each mechanical part of the aircraft into our model and would thus acquire an almost white-box model of the system. However, the computational cost of adding such a huge amount of detail would effectively inhibit the usage of such a model. Additionally, the uncertainty would increase due to an overly complex system, because each separate part induces some amount of variance into the model. It is therefore usually appropriate to make some approximations to reduce the model to a sensible size. Engineers often can accept some approximations in order to get a more robust and simple model. For example Newton's classical mechanics is an approximated model of the real world. Still, Newton's model is quite sufficient for most ordinary-life situations, that is, as long as particle speeds are well below the speed of light, and we study macro-particles only.

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2.14.6. Training

Any model which is not pure white-box contains some parameters that can be used to fit the model to the system it shall describe. If the modeling is done by a neural network, the optimization of parameters is called training. In more conventional modeling through explicitly given mathematical functions, parameters are determined by curve fitting.

2.14.7. Model Evaluation

A crucial part of the modeling process is the evaluation of whether or not a given mathematical model describes a system accurately. This question can be difficult to answer as it involves several different types of evaluation.

2.14.8. Fit to Empirical Data

Usually the easiest part of model evaluation is checking whether a model fits experimental measurements or other empirical data. In models with parameters, a common approach to test this fit is to split the data into two disjoint subsets: training data and verification data. The training data are used to estimate the model parameters. An accurate model will closely match the verification data even though this data was not used to set the model's parameters. This practice is referred to as cross-validation in statistics. Defining a metric to measure distances between observed and predicted data is a useful tool of assessing model fit. In statistics, decision theory, and some economic models, a loss function plays a similar role. While it is rather straightforward to test the appropriateness of parameters, it can be more difficult to test the validity of the general mathematical form of a model. In general, more mathematical tools have been developed to test the fit of statistical models than models involving Differential equations. Tools from nonparametric statistics can sometimes be used to evaluate how well data fits a known distribution or to come up with a general model that makes only minimal assumptions about the model's mathematical form.

2.14.9. Scope of the Model

Assessing the scope of a model, that is, determining what situations the model is applicable to, can be less straightforward. If the model was constructed based on a set of data, one must determine for what systems or situations the data is a typical set of data from. The question of whether the model describes well the properties of the system between data points is called interpolation, and the same question for events or data points outside the observed data is called extrapolation. As an example of the typical limitations of the scope of a model, in evaluating Newtonian classical mechanics, we can note that Newton made his measurements without advanced equipment, so he could not measure properties of particles travelling at speeds close to the speed of light. Likewise, he did not measure the movements of molecules and other small particles, but macro particles only. It is then not surprising that his model does not extrapolate

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well into these domains, even though his model is quite sufficient for ordinary life physics.

2.14.10. Philosophical Considerations

Many types of modeling implicitly involve claims about causality. This is usually (but not always) true of models involving differential equations. As the purpose of modeling is to increase our understanding of the world, the validity of a model rests not only on its fit to empirical observations, but also on its ability to extrapolate to situations or data beyond those originally described in the model. One can argue that a model is worthless unless it provides some insight which goes beyond what is already known from direct investigation of the phenomenon being studied. An example of such criticism is the argument that the mathematical models of Optimal foraging theory do not offer insight that goes beyond the common-sense conclusions of evolution and other basic principles of ecology.

2.15. Participant Observation

Participant observation is a set of research strategies which aim to gain a close and intimate familiarity with a given group of individuals (such as a religious, occupational, or sub-cultural group, or a particular community) and their practices through an intensive involvement with people in their natural environment, often though not always over an extended period of time. It is similar to ethnography but often involves a shorter time in the field.

2.15.1. Method and Practice

Such research usually involves a range of methods: informal interviews, direct observation, participation in the life of the group, collective discussions, analyses of personal documents produced within the group, self-analysis, and life-histories. Although the method is generally characterized as qualitative research, it can (and often does) include quantitative dimensions. Participant observation is usually undertaken over an extended period of time, ranging from several months to many years. An extended research time period means that the researcher will be able to obtain more detailed and accurate information about the people he/she is studying. Observable details (like daily time allotment) and more hidden details (like taboo behavior) are more easily observed and understandable over a longer period of time. A strength of observation and interaction over long periods of time is that researchers can discover discrepancies between what participants say -- and often believe -- should happen (the formal system) and what actually does happen, or between different aspects of the formal system; in contrast, a one-time survey of people's answers to a set of questions might be quite consistent, but is less likely to show conflicts between different aspects of the social system or between conscious representations and behavior.

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2.15.2. Variations and Related Methods

A variant of participant observation is observing participation. The sociological methods known as grounded theory (Glazer and Strauss) overlap significantly with the more formalized versions of participant observation.

2.16. Phenomenology

The term phenomenology in science is used to describe a body of knowledge which relates several different empirical observations of phenomena to each other, in a way which is consistent with fundamental theory, but is not directly derived from theory. It is a theory which expresses mathematically the results of observed phenomena without paying detailed attention to their fundamental significance.

2.16.1. Phenomenology in Physical Sciences

There are cases in physics when it is not possible to derive a theory for describing observed results using first principles (such as Newton's laws of motion or Maxwell's equations of electromagnetism). There may be several reasons for this: For example, the underlying theory is not yet understood or non-existent or the mathematics to describe the observations is too complex. Sometimes different length, mass and time scales are used to build a phenomenological theory. In these cases sometimes simple algebraic expressions may be used to model observations or experimental results and used to make predictions about the results of other observations or experiments, despite the fact that the expressions themselves cannot be (or have not yet been) derived from the fundamental theory of that domain of knowledge. Another way of describing phenomenology is that it is intermediate between experiment and theory. It is more abstract and includes more logical steps than experiment, but is more directly tied to experiment than theory. The boundaries between theory and phenomenology, and between phenomenology and experiment, are somewhat fuzzy and to some extent depend on the preconceptions of the scientist describing these and the particular field in which the scientist works.

2.16.2. Phenomenology in Social Statistics

In the science of Statistics, the collection of quantifiable data from people involves a phenomenological step. In order to obtain that data, survey questions must be designed to collect measurable responses which are categorized in a logically sound and practical way, such that the form in which the questions are asked does not bias the results. If this is not done, data distortions due to question-wording effects (response error) occur, and the data obtained may have no validity at all, because observations are counted up which do not have the same meaning (it would be like "adding up apples and pears"). A prerequisite of a good survey is that all respondents are really able to

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give a definite and unambiguous answer to the questions, and that they understand what is asked of them in the same way. One could for example ask farmers "How much risk do you run on your farm?" with a scale of response options ranging from e.g. "a lot of risk" to "no risk". But this yields quantitatively meaningless data which is not objective, since the interpretations of risk by farmers could focus on e.g. on the number, size, frequency, severity or consequence of risks, and each farmer will have his own idiosyncratic idea about that. All farmers may suffer e.g. from a lack of rainfall, but some will personally consider it a large risk, others a low risk and some not a risk at all. Furthermore, in actually asking the questions of respondents and subsequently coding the responses to numerical values, a technique must be found to ensure that no misinterpretation occurs of a type that would lead to errors. In other words, in designing the survey instrument, the researcher must somehow find a satisfactory "bridge" of meaning between the logical and practical requirements of the survey statistician, a statistical classification scheme, the awareness of respondents and the processors of the raw data. Finding this "bridge" involves an abstraction process which necessarily goes beyond logical inference, theory and experiment and involves an element of "art", because it must establish an appropriate connection between the language used, the inter-subjective interactions between the surveyor and the respondent, and how respondents and those who process the data construct the meaning of what is being asked of them. For this cognitive process, it is impossible to provide a standard procedure which will always work, only "rules of thumb"; it requires a "practical" human insight.

2.17. Q Methodology

Q Methodology is a research method used in psychology and other social sciences to study people's "subjectivity" -- that is, their viewpoint. Q was developed by psychologist William Stephenson. It has been used both in clinical settings for assessing patients, as well as in research settings to examine how people think about a topic. The name "Q" comes from the form of factor analysis that is used to analyze the data. Normal factor analysis, called "R method," involves finding correlations between variables (say, height and age) across a sample of subjects. Q, on the other hand, looks for correlations between subjects across a sample of variables. Q factor analysis reduces the many individual viewpoints of the subjects down to a few "factors," which represent shared ways of thinking. It is sometimes said that Q factor analysis is R factor analysis with the data table turned sideways. While helpful as a heuristic for understanding Q, this explanation may be misleading, as most Q methodologists argue that for mathematical reasons no one data matrix would be suitable for analysis with both Q and R.

One salient difference between Q and other social science research methodologies, such as surveys, is that it typically uses many fewer subjects. This can be strength, as Q is sometimes used with a single subject. In such cases, a person will rank the same set of statements under different conditions of instruction. For example, someone might be given a set of statements about personality traits and then asked to rank them according to how well they describe herself, her ideal self, her father, her mother, etc. In studies of intelligence, Q factor analysis can generate consensus based assessment

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(CBA) scores as direct measures. Alternatively, the unit of measurement of a person in this context is his factor loading for a Q-sort he or she performs. Factors represent norms with respect to schemata. The individual who gains the highest factor loading on an Operant factor is the person most able to conceive the norm for the factor.

2.18. Questionnaire

A questionnaire is a research instrument consisting of a series of questions and other prompts for the purpose of gathering information from respondents. Although they are often designed for statistical analysis of the responses, this is not always the case. Questionnaires have advantages over some other types of surveys in that they are cheap, do not require as much effort from the questioner as verbal or telephone surveys, and often have standardized answers that make it simple to compile data. However, such standardized answers may frustrate users. Questionnaires are also sharply limited by the fact that respondents must be able to read the questions and respond to them. Thus, for some demographic groups conducting a survey by questionnaire may not be practical. As a type of survey, questionnaires also have many of the same problems relating to question construction and wording that exist in other types of opinion polls.

2.19. Simulation

Simulation is the imitation of some real thing, state of affairs, or process. The act of simulating something generally entails representing certain key characteristics or behaviors of a selected physical or abstract system. Simulation is used in many contexts, including the modeling of natural systems or human systems in order to gain insight into their functioning. Other contexts include simulation of technology for performance optimization, safety engineering, testing, training and education. Simulation can be used to show the eventual real effects of alternative conditions and courses of action. Key issues in simulation include acquisition of valid source information about the referent, selection of key characteristics and behaviors, the use of simplifying approximations and assumptions within the simulation, and fidelity and validity of the simulation outcomes.

2.19.1. Simulation in Education and Training

Simulation is often used in the training of civilian and military personnel. This usually occurs when it is prohibitively expensive or simply too dangerous to allow trainees to use the real equipment in the real world. In such situations they will spend time learning valuable lessons in a "safe" virtual environment. Often the convenience is to permit mistakes during training for a safety-critical system. Training simulations typically come in one of three categories:

"live" simulation (where real people use simulated (or "dummy") equipment in the real world);

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“virtual" simulation (where real people use simulated equipment in a simulated world, or virtual environment, or

"constructive" simulation (where simulated people use simulated equipment in a simulated environment). Constructive simulation is often referred to as "war gaming" since it bears some resemblance to table-top war games in which players command armies of soldiers and equipment that move around a board.

In standardized tests, "live" simulations are sometimes called "high-fidelity", producing "samples of likely performance", as opposed to "low-fidelity", "pencil-and-paper" simulations producing only "signs of possible performance”, but the distinction between high, moderate and low fidelity remains relative, depending on the context of a particular comparison. Simulations in education are somewhat like training simulations. They focus on specific tasks. The term 'micro-world' is used to refer to educational simulations which model some abstract concept rather than simulating a realistic object or environment, or in some cases model a real world environment in a simplistic way so as to help a learner develop an understanding of the key concepts.

2.19.2. Clinical Healthcare Simulators

Medical simulators are increasingly being developed and deployed to teach therapeutic and diagnostic procedures as well as medical concepts and decision making to personnel in the health professions. Simulators have been developed for training procedures ranging from the basics such as blood draw, to laparoscopic surgery and trauma care. They are also important to help on prototyping new devices for biomedical engineering problems. Currently, simulators are applied to research and development of tools for new therapies, treatments and early diagnosis in medicine. Many medical simulators involve a computer connected to a plastic simulation of the relevant anatomy. Sophisticated simulators of this type employ a life size mannequin that responds to injected drugs and can be programmed to create simulations of life-threatening emergencies. In other simulations, visual components of the procedure are reproduced by computer graphics techniques, while touch-based components are reproduced by haptic feedback devices combined with physical simulation routines computed in response to the user's actions. Medical simulations of this sort will often use 3D CT or MRI scans of patient data to enhance realism. Some medical simulations are developed to be widely distributed (such as web-enabled simulations that can be viewed via standard web browsers) and can be interacted with using standard computer interfaces, such as the keyboard and mouse. Another important medical application of a simulator — although, perhaps, denoting a slightly different meaning of simulator — is the use of a placebo drug, a formulation that simulates the active drug in trials of drug efficacy.

2.19.3. Engineering Technology or Process Simulation

Simulation is an important feature in engineering systems or any system that involves many processes. For example in electrical engineering, delay lines may be used to simulate propagation delay and phase shift caused by an actual transmission line.

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Similarly, dummy loads may be used to simulate impedance without simulating propagation, and is used in situations where propagation is unwanted. A simulator may imitate only a few of the operations and functions of the unit it simulates. Contrast with: emulate. Most engineering simulations entail mathematical modeling and computer assisted investigation. There are many cases, however, where mathematical modeling is not reliable. Simulation of fluid dynamics problems often requires both mathematical and physical simulations. In these cases the physical models require dynamic similitude. Physical and chemical simulations have also direct realistic uses, rather than research uses; in chemical engineering, for example, process simulations are used to give the process parameters immediately used for operating chemical plants, such as oil refineries.

2.20. Statistics

Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. Also with prediction and forecasting based on data. It is applicable to a wide variety of academic disciplines, from the natural and social sciences to the humanities, government and business. Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and are then used to draw inferences about the process or population being studied; this is called inferential statistics. Descriptive, predictive, and inferential statistics comprise applied statistics. There is also a discipline called mathematical statistics, which is concerned with the theoretical basis of the subject. Moreover, there is a branch of statistics called exact statistics that is based on exact probability statements.

In applying statistics to a scientific, industrial, or societal problem, one begins with a process or population to be studied. This might be a population of people in a country, of crystal grains in a rock, or of goods manufactured by a particular factory during a given period. It may instead be a process observed at various times; data collected about this kind of "population" constitute what is called a time series. For practical reasons, rather than compiling data about an entire population, one usually studies a chosen subset of the population, called a sample. Data are collected about the sample in an observational or experimental setting. The data are then subjected to statistical analysis, which serves two related purposes: description and inference.

Descriptive statistics can be used to summarize the data, either numerically or graphically, to describe the sample. Basic examples of numerical descriptors include the mean and standard deviation. Graphical summarizations include various kinds of charts and graphs.

Inferential statistics is used to model patterns in the data, accounting for randomness and drawing inferences about the larger population. These inferences may take the form of answers to yes/no questions (hypothesis testing), estimates of numerical characteristics (estimation), descriptions of association (correlation), or

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modeling of relationships (regression). Other modeling techniques include ANOVA, time series, and data mining.

The use of any statistical method is valid only when the system or population under consideration satisfies the basic mathematical assumptions of the method. Misuse of statistics can produce subtle but serious errors in description and interpretation — subtle in the sense that even experienced professionals sometimes make such errors, serious in the sense that they may affect, for instance, social policy, medical practice and the reliability of structures such as bridges. Even when statistics is correctly applied, the results can be difficult for the non-expert to interpret.

2.20.1. Statistical methods

A. Experimental and observational studies

A common goal for a statistical research project is to investigate causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables or dependent variables on response. There are two major types of causal statistical studies: experimental studies and observational studies. In both types of studies, the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies in how the study is actually conducted. Each can be very effective. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Instead, data are gathered and correlations between predictors and response are investigated.

The basic steps of an experiment are;

1. Planning the research, including determining information sources, research subject selection, and ethical considerations for the proposed research and method.

2. Design of experiments, concentrating on the system model and the interaction of independent and dependent variables.

3. Summarizing a collection of observations to feature their commonality by suppressing details. (Descriptive statistics)

4. Reaching consensus about what the observations tell about the world being observed. (Statistical inference)

5 Documenting / presenting the results of the study.

B. Levels of measurement

There are four types of measurements or levels of measurement or measurement scales used in statistics: nominal, ordinal, interval, and ratio. They have different

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degrees of usefulness in statistical research. Ratio measurements have both a zero value defined and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data. Interval measurements have meaningful distances between measurements defined, but have no meaningful zero value defined (as in the case with IQ measurements or with temperature measurements in Fahrenheit. Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values. Nominal measurements have no meaningful rank order among values.

Since variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are called together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative or continuous variables due to their numerical nature.

2.20.2. Statistical techniques

Some well known statistical tests and procedures are: Student's t-test chi-square test Analysis of variance (ANOVA) Mann-Whitney U Regression analysis Factor Analysis Correlation Pearson product-moment correlation coefficient Spearman's rank correlation coefficient Time Series Analysis Mean Square Weighted Deviation MSWD

2.21. Statistical Surveys

Statistical surveys are used to collect quantitative information about items in a population. Surveys of human populations and institutions are common in political polling and government, health, social science and marketing research. A survey may focus on opinions or factual information depending on its purpose, and many surveys involve administering questions to individuals. When the questions are administered by a researcher, the survey is called a structured interview or a researcher-administered survey. When the questions are administered by the respondent, the survey is referred to as a questionnaire or a self-administered survey.

2.21.1. Structure and standardization

The questions are usually structured and standardized. The structure is intended to reduce bias. For example, questions should be ordered in such a way that a question

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does not influence the response to subsequent questions. Surveys are standardized to ensure reliability, generalizability, and validity. Every respondent should be presented with the same questions and in the same order as other respondents.

2.21.2. Serial Surveys

Serial surveys are those which repeat the same questions at different points in time, producing time-series data. They typically fall into two types:

Cross-sectional surveys which draw a new sample each time. In a sense any one of survey will also be cross-sectional.

Longitudinal surveys where the sample from the initial survey is re-contacted at a later date to be asked the same questions.

2.21.3. Modes of Data Collection

There are several ways of administering a survey, including: Telephone Mail

the questionnaire may be handed to the respondents or mailed to them, but in all cases they are returned to the researcher via mail.

not suitable for very complex issues no interviewer bias introduced

Online surveys can use web or e-mail often inexpensive to administer very fast results easy to modify data creation, manipulation and reporting can be automated and/or easily exported

into a format which can be read by PSPP, DAP or other statistical analysis software data sets created in real time

2.21.4. Sampling

Sample selection is critical to the validity of the information that represents the populations that are being studied. The approach of the sampling helps to determine the focus of the study and allows better acceptance of the generalizations that are being made. Careful use of biased sampling can be used if it is justified and as long as it is noted that the resulting sample may not be a true representation of the population of the study. There are two different approaches to sampling in survey research, namely non-probability and probability samplings.

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Non-probability sampling does not guarantee the chance that all the elements involved in the research will be included in the sample. We can not calculate the probability that each element will be represented. The most commonly used non-probability sampling method is the convenience sampling approach. With this method, it only samples those who are available and willing to participate in the survey. The use of this approach allows for convenience for the researcher and a possible small sample while possibly losing data validity due to the lack of representation.

The probability sampling approach for research methods gives each element an equal chance of being included in the sample. This method is closer to a true representation of the population. It can be difficult to use due to size of the sample and cost to obtain, but the generalizations that come from it are more likely to be closer to the a true representation of the population. Probability sampling includes specific sampling procedures such as simple random sampling and stratified random sampling that allow the sample to represent the population more than the non-probability approach.

Simple random sampling approach, each element of the population has an equal chance of being included in the sample.

Stratified random sampling approach, the population is divided into subpopulations (called strata) and the random samples are then drawn from the strata. This approach increases the representation of the population.

Research is often conducted using the hourglass model. The hourglass model starts with a broad spectrum for research, focusing on the required information through the methodology of the project (like the neck of the hourglass), then expands the research in the form of discussion and results.

3. Writing Research Paper

Despite the illusion, the research-paper writing process (as with any writing process) is quasi-linear at best. The entire process will require a lot of hard work on your part, but the results will be more than satisfying if you give it your best.

3.1. Genre

Usually at the post-secondary level, when it finally comes time to write your first real research essay--or "paper" as it's more commonly called--you may find yourself confronted with confusion, resentment, panic, and a touch . True research papers are more than a loose collection of anecdotal memories or a patchwork of data pulled from several books. But while new to most first-year students, a research paper can be incredibly exciting, rewarding, and even comforting to write because it finally allows you to really get into a subject you care about with both hands while having added security--a proverbial squad car of "back up" to support you while you explore those dark alleyways of future knowledge. Research papers come in all shapes, sizes, forms, and disciplines.

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3.2. Topic

Remember: unless otherwise specified that you must only choose from the options given to you, take the initiative to propose an appropriate topic based on your own motivation. Have faith in your own smarts and course work. You'll start the research in the next step. Nobody's expecting you to be an expert or get your paper published in a journal, so just start jotting down ideas about things related to your topic. You may even want to keep a journal to keep everything in one place. So step one is to relax. Be sure though not just to put down things you think you should write about or might want to write about but basically just anything that comes to mind when you look at the topic. The important thing here is not to edit your meanderings; this is not the step for second-guessing what you've written. Connotations, associations, related concepts, connections--that's what you're looking for to get a topic. The real key to successful papers that you can actually enjoy writing is motivation, which is why your topic choice is so important. During your idea-generation activities, once you have started seeing great things jumping out at you, finish your "session" and then make a list of why a potential topic is important. To do this: First think of yourself--is this something you believe in? that sounds fun? that you sincerely want to learn more about? that intrigues you? Even when you're given a set topic in advance, you can always frame it to suit your needs and style--so get something out of it.

Then think of the audience - will other people familiar with this subject care to read what you're writing? Do you have something to say or are you babbling and wasting space?

3.3. Scope

After the chaotic armchair free-for-all of the previous step, this step basically covers (1) preliminary research and then (2) some real refining of your topic. Why does preliminary research before the real nitty-gritty stuff a couple steps away? Some of the functions include:

library familiarization: getting to know where things are and dipping your toes into the whole research pool before diving in head-first

fascination with a topic that you'd like to pursue further but don't know enough about examination of the available resources--even if you're familiar with the general

subject area and the library--to see how feasible delving further into the topic will prove

Preliminary research though is the first real time in the whole process where you'll be forced to match the internal (what comes from your mind during the previous step) with the external (the realities you're going to be faced with).

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3.4. Thesis

Tips for Writing Your Thesis Statement

1. Determine what kind of paper you are writing: An analytical paper breaks down an issue or an idea into its component parts,

evaluates the issue or idea, and presents this breakdown and evaluation to the audience.

An expository (explanatory) paper explains something to the audience. An argumentative paper makes a claim about a topic and justifies this claim with

specific evidence. The claim could be an opinion, a policy proposal, an evaluation, a cause-and-effect statement, or an interpretation. The goal of the argumentative paper is to convince the audience that the claim is true based on the evidence provided. If you are writing a text which does not fall under these three categories (ex. a narrative), a thesis statement somewhere in the first paragraph could still be helpful to your reader.

2. Your thesis statement should be specific—it should cover only what you will discuss in your paper and should be supported with specific evidence.

3. The thesis statement usually appears at the end of the first paragraph of a paper. 4. Your topic may change as you write, so you may need to revise your thesis

statement to reflect exactly what you have discussed in the paper.

3.5. Research

With tentative thesis statement or research question in hand, you've got what will likely become the focal point of your paper. You have a focus, a goal, a purpose--in essence, the bones of your essay. But now you need flesh for those bones; that's where research comes in.

Before jumping into that pool, you may be asking, "Why not do an outline first?" If you're doing an argumentative paper, chances are that you already have some mental notes about your topic's "sub-components" (the ones that might eventually break down into supporting paragraphs); it was probably those informal sub-points or reasons that helped you formulate your argument in the first place. Research-question writers probably have only vague ideas of what they might possibly come across in the debates they're analyzing. In either case though, we suggest putting together an outline after you do research. You don't want to narrow yourself too much at this point. A very clear thesis or question gives you enough direction to keep you on task, but still leaves you open to new angles on the subject.

To conduct research, follow the advice outlined in these three important steps:

Understand the types of resources Critically read and evaluate those sources

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Note-take effectively

3.5.1. Understand the types of resources

Your sources, or the materials which supply you information, are your resources. It is useful to enter the research process with the positive attitude that your sources are in your corner to help you flesh out your paper and open your eyes to a "collective wealth" of knowledge (the second definition of resource!), not just in there as paper requirements. For argumentative papers, sources act as evidence to back up your thesis. For analytical papers, sources act more as possible answers to your research question. For the sake of simplicity, we will refer to this dual function of sources with one word: support. There are two types of support: primary and secondary. A primary source is an original document or account that is not about another document or account but stands on its own. For example, any novel, poem, play, diary, letter, or other creative work is a primary source. The data from a research study also constitutes a primary source because it comes straight from the participants' replies. Interviews, not of experts but of people actually experiencing something "on the scene," are also primary sources. Secondary sources are ones that interpret primary sources or are otherwise a step removed. A journal article or book about a poem, novel, or play or a commentary about what an interview signifies is a secondary source. Your paper will likewise become a secondary source.

3.5.2. Critically read and evaluate the sources

Now that you have some materials in front of you, either at the library or at home, it's time to critically analyze them. You need to know what is happening in the text before you take formal notes since part of analyzing means sifting the good resources from the bad. Read the sources critically. Structure, purpose, audience, and author are four important dimensions of the text to pay close attention to.

Structure

If you're starting with a book, look at the table of contents. See the shape of what's to come and identify places that your thesis or question might be most directly addressed. Notice the subsections. Is there anything very obviously missing? Skim the Preface or Introduction to establish context for the discussion and determine the author's intent. The author's thesis statement just may pop up here; be on the lookout for it. Or, it may be implied; if so, why?

Glance at any appendices, diagrams, tables, or figures and see what kinds of things make it into the Endnotes section if there is one. Look at the topics listed in the Index at the back. Which of the entries has the most page numbers listed next to it? This will give you an indication of the subjects that contribute to the real scope of the book.

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For a journal article, read the "abstract" for a summary. If it seems to address your question or thesis, then read the Background or Introduction section which will normally have some kind of "literature review" or summary of what others have said. This context is useful for seeing how and why the issue has evolved over time. Conclusions or Discussions are a great place to turn to next before getting bogged down in minute detail. Did the author answer the research question or support the thesis? If you can clearly see where the article was intending to go and where it ended up, then you can go back and read the body for details. Starting with the intro and conclusion is a good strategy for analyzing essays as well, online or otherwise.

Purpose

Examine the title and first few paragraphs. What is the author trying to do? What is his or her bias? Any assumptions to be challenged? Look at the publisher or institutional/organizational affiliation of the author. Does the person have a vested interest in swaying you one way or another? A book on management style will be markedly different if it comes from some corporate management committee compared to union representatives. It would even be different presented by a professor of economics rather than a professor specializing in human relations and organizational psychology. Authors should be upfront about the angles they take in their discussions. Is yours?

Audience

Who does the intended audience appear to be? How narrow or broad is it? To answer this, look at stylistic choices such as diction and tone. For instance, are there a lot of technical words? If so, look them up. And finally, what stake does the target audience have in the issue? In other words, why would the audience be reading the text? Who would you be imagining yourself talking to in your paper?

Author

Who is the author? Is it someone your professor has mentioned or whom you've come across in your course readings? Has the person been mentioned in other texts or bibliographies of other texts? Presence in the scholarly community is one of the ways to establish authority. Another is education and/or expertise. Is the person a teacher or researcher from a reputable academic institution? Does the person have considerable knowledge of what he or she is talking about? Is the author respected and well-received? You wouldn't let just anyone off the street walk into your home, so make your sources establish rapport and trust with you before you just let them walk on into your research paper.

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3.5.3. Note-take effectively

You already started the process of note-taking in the previous section, even before putting pen to paper. How? Well, to take notes, you need to know what to take notes on; by analyzing the text, you've likely already located the sections or chapters most useful to you.

What should my notes look like? What should I write down? Specific tips to avoid plagiarism Specific tips to facilitate comprehension later on

What should my notes look like?

The point-form or sentences debate (on loose-leaf or on 3x5 index cards for easy shuffling) is simply a matter of preference. Some students are comfortable with points; others prefer summarizing and paraphrasing right into rough sentences to make drafting easier. Try both methods and see which one you prefer. Note-taking involves writing. Highlighting can be an important first step, but used alone, it's simply too passive.

So what should I write down?

Write down anything and everything that will flesh out your thesis statement or research question. Remember that it's fine to copy down duplicating facts. You may need them later on to defend your thesis. For major issues, having more than one person who agrees with you strengthens your point. Just make sure to record who said what each time. It's also okay to copy down contradictory information. Analytical papers often include opposing views and even for argumentative papers, acknowledging an opposing viewpoint that is easily disproved by its counterpoint is always a good rhetorical tool. Whatever you take notes on, be sure to take them from more than one or two key sources. Using a variety will lend weight to your argument, broaden your horizons on the topic when you need varying viewpoints anyway, and demonstrate to your professor the thoroughness of your research.

The final piece of data to record is a working bibliography of all the sources you consult. Begin jotting one down as soon as you begin researching so that you won't forget when it comes time to draft the paper (a common error and stress-inducer). Therefore, before you even take notes, neatly record all the pertinent bibliographical information you'll need for any citation format you decide to use (author, title, (editor, translator, and/or edition number if there is one), publisher, city of publication, year of publication, issue number, volume, and page numbers).

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3.6. Developing an Outline

You should follow these four suggestions to create an effective outline. The examples are taken from the Sample Outline handout. 1. Parallelism: Each heading and subheading should preserve parallel structure. If the

first heading is a verb, the second heading should be a verb. Example: Choose desired colleges Prepare application

("Choose" and "Prepare" are both verbs. The present tense of the verb is usually the preferred form for an outline)

2. Coordination: All the information contained in Heading 1 should have the same significance as the information contained in Heading 2. The same goes for the subheadings (which should be less significant than the headings). Example:

Visit and evaluate college campuses Visit and evaluate college websites

Note important statistics Look for interesting classes

(Campus and websites visits are equally significant. They are part of the main tasks you would need to do. Finding statistics and classes found on college websites are parts of the process involved in carrying out the main heading topics.)

3. Subordination: The information in the headings should be more general, while the

information in the subheadings should be more specific. Example: Describe an influential person in your life

Favorite high school teacher Grandparent

(A favorite teacher and grandparent are specific examples from the generalized category of influential people in your life.)

4. Division: Each heading should be divided into 2 or more parts. Example:

Compile resume List relevant coursework List work experience List volunteer experience

(The heading "Compile resume" is divided into 3 parts.) Technically, there is no limit to the number of subdivisions for your headings; however, if you seem to have a lot, it may be useful to see if some of the parts can be combined.

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3.7. First Draft

Before you begin writing, you should have a thesis or question that you're comfortable with and an outline that gives you structure on what you need to say and where. Now just take pen to paper or fingers to keyboard and write. "Sure, easier said than done," you might be thinking. Fair enough, but we aren't asking you to come up with polished prose. It can be as rough as you want it to be. And with practice, it does get easier and faster. Believe it or not, drafting should be the least time-consuming step in the research paper process. Invention should take longer. Research should take longer. And revising should definitely take longer. If it's taking you a month of Sundays just to eke out a thousand words, two things could be happening: 1. You don't have any clue what you should be saying (in which case you don't have a

focal point or outline yet and so are starting too early!) or . . . 2. You're revising while you draft so that you end up with one sentence an hour. If it's the latter

(as it often is), separate your duties out. Within every writer, there is a Creator and a Critic. Write a letter to your Critic telling him or her to go to sleep for this step and wake up for the next one. Let your Creator shine for now.

3.8. Revision

Now is the time to become your own audience and evaluate your work. After letting your draft sit for a few days, look at your work with a new critical eye, critical for what doesn't work and what does. Before you go over the heuristic we've devised below to help you revise, remember that revision is not proofreading. Revision deals with underlying issues and content while proofreading deals largely with surface details and presentation. Like a funnel, you have to start at "higher order" concerns (how the essay and individual paragraphs hold together) and then move down to "lower order" concerns (sentences, word choice, mechanics).

ASK YOURSELF . . .

Does your title give readers a good idea of what's to come? (Have you even come up with one yet? Is your thesis statement or research question clearly stated? Is there enough lead-ins in the introduction to establish the importance of and context for the statement/question? Is there too much? Too little? By the end of the introduction, is it clear to the audience what kind of material will follow? If so, are these expectations fulfilled, that is, do you follow through? Is it clear where your introduction ends and body begins and where the body ends and the conclusion begins? In other words, are your paragraph indents meaningful?

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At the same time, are there transitions between all sections and paragraphs to create flow and unity? Does each body paragraph have a topic sentence? If you took your thesis/question and all your topic sentences, would that correspond to what you want to say in your paper? If not, do you need to revise your thesis/question or re-examine your sub-points? Do the topic sentences (1) make a connection back with the thesis/question, (2) establish a link with the previous paragraph's content (perhaps the chronological relationship, any comparisons/contrasts?) and (3) give enough information that the audience could guess where a particular paragraph's development would lead? With or without a formal concluding sentence, do you somewhere near the end of each paragraph remind readers why you are saying what you are saying by moving back up to abstract, general terms? Does the order of paragraphs make sense? (e.g., maybe the transitions seem forced because they aren't in the right order) Are your paragraphs too short (say, fewer than 4 sentences) or too long ( longer than about 8)? Is there some combining or separating of issues that needs to take place? Or do you simply need to generate more content or delete irrelevant material? Are your examples reliable, representative, and convincing? Are there enough of them (or too many) to develop the main idea of the paragraph in the word count you have available? Are your sources convincing? Is there enough balance between your own insights and expert opinions? Is anything that should be referenced, referenced? Are all sources and direct quotations explained or have you left them standing on their own? Has anything that goes off topic or is not essential (given your word limit) been cut? (TIP: whenever you know you have to cut something but you're finding it hard to do, cut and paste it in a separate file so that you feel it hasn't been obliterated. In a couple of weeks, you'll probably go back and wonder why you were so attached to the passage in the first place!) Does the conclusion say something different from your introduction? Does it leave a good lasting impression or is it wishy-washy?

There are 4 basic actions that will occur during the revisions you now hopefully plan to make:

ADD. Insert needed words, sentences, and paragraphs. If your additions require new content, return to the idea-gathering techniques.

CUT. Get rid of whatever goes off the topic or repeats what has already been said.

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REPLACE. As needed, substitute new words, sentences, and paragraphs for what you have cut.

MOVE MATERIAL AROUND. Change the sequence of paragraphs if the material is not presented in logical order. Move sentences.

All of these actions are easily done electronically, but try not to do all your revision on the computer. Alternating between "screen" and "paper" copy is a great way to achieve perspective.

Now what about 'lower order' concerns? These issues are highly individualized so look through old marked papers for comments you received at the level of sentences and diction (word choice). Are there any trends you notice? Bring in a writing sample to a tutor and we can examine a piece for you and look for things you both do well and seem to have difficulty with. The most common mistakes are a lack of clarity (perhaps because you're trying to sound "academic" or have forgotten that you're writing to an audience) and general wordiness.

3.9. Proofreading

Believe it or not, now that you've hopefully finished major revisions, the hardest part is really over! Your goal at this point is not so much to focus on content but on nitpicky copyediting which is so great for catching those careless mistakes that distract your readers from your main ideas. Here's a checklist for some finishing touches: Check out your verb tenses. Don't feel you have to completely avoid the "passive" tense (e.g., "the ball was caught") but definitely try to have MORE subject-verb "active" sentences; they add power and agency to your writing (e.g., "Billy caught the ball"). Also make sure your verbs are in the right tense. If you're talking about literature, keep the tense in what is called "the literary present." So a sentence in your essay to set up an example would read "When Hana tells Caravaggio about the English patient..." If you're writing a historical paper though, past tense is more suitable. Check for non-sexist language, especially in pronoun situations (e.g., "What does an artist look for in his (er, her...er, their...ARRRGHHH) imagery?"). The best way is to talk to your professors. You'll find some that say they don't mind the awkward "him/her" (or "him or her") split, others who prefer one over the other, and still others who want you to avoid the sticky scenario altogether. Figure out preferences. Read your essay out loud to listen for either awkward or long sentences that could be clarified or broken up to read better. Check your punctuation. Look for glaring grammatical flaws. Check your diction (word choice). If you're looking for a better word, look up some possibilities in dictionary.

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Prepare a Works Cited or References list. Set up footnotes or endnotes if you need them too. Now you can check your spelling both with a computer spell-checker and with your own eyes to catch those words that are spelled right but used in the wrong context (like there vs. their vs. they're). Work on the presentation of your paper: use a laser-printer if you can (or else your best ink-jet) on 8.5 x 11 inch paper, double space your lines, maintain 1 inch margins, start numbering pages on the second page of actual text, and prepare a title page with an original title somewhere in the centre and your vital student info in the bottom right hand corner. Also make sure your font is very readable (Times New Roman is the most common) and in 12 point.

4. Proposal Writer's Guide

4.1. Introduction

Writing a proposal for a sponsored activity such as a research project or a curriculum development program is a problem of persuasion. It is well to assume that your reader is a busy, impatient, skeptical person who has no reason to give your proposal special consideration and who is faced with many more requests than he can grant, or even read thoroughly. Such a reader wants to find out quickly and easily the answers to these questions. What do you want to do, how much will it cost, and how much time will it take? How does the proposed project relate to the sponsor's interests? What difference will the project make to: your university, your students, your discipline, the state, the nation, the world, or whatever the appropriate categories are? What has already been done in the area of your project? How do you plan to do it? How will the results be evaluated? Why should you, rather than someone else, do this project? These questions will be answered in different ways and receive different emphases depending on the nature of the proposed project and on the agency to which the proposal is being submitted. Most agencies provide detailed instructions or guidelines concerning the preparation of proposals (and, in some cases, forms on which proposals are to be typed); obviously, such guidelines should be studied carefully before you begin writing the draft.

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4.2. Parts of a Proposal

Proposals for sponsored activities follow generally a similar format, although there are variations depending upon whether the proposer is seeking support for a research grant, a training grant, or a conference or curriculum development project. The following outline and explanation concern chiefly the components of a research proposal. This section concludes with a discussion of certain variations in format required if one is seeking support for other kinds of academic programs.

4.2.1. Research Proposals

Typical parts of a research proposal are: Title (or Cover) Page Abstract Table of Contents Introduction (including Statement of Problem, Purpose of Research, and Significance of Research)Background (including Literature Survey)Description of Proposed Research (including Method or Approach)Description of Relevant Institutional Resources List of References Personnel Budget

Title (or Cover) Page

Most sponsoring agencies specify the format for the title page, and some provide special forms to summarize basic administrative and fiscal data for the project. Generally, the principal investigator, his or her department head, and an official representing the University sign the title page. In addition, the title page usually includes the University's reference number for the proposal, the name of the agency to which the proposal is being submitted, the title of the proposal, the proposed starting date and budget period, the total funds requested, the name and address of the University unit submitting the proposal, and the date submitted. Some agencies want the title page to specify whether the proposal is for a new or continuing project. And some ask to which other agencies the proposal is being submitted. A good title is usually a compromise between conciseness and explicitness. Although titles should be comprehensive enough to indicate the nature of the proposed work, they should also be brief. One good way to cut the length of titles is to avoid words that add nothing to a reader's understanding, such as "Studies on...," "Investigations...," or "Research on Some Problems in...."

Abstract

Every proposal, even very brief ones, should have an abstract. Some readers read only the abstract, and most readers rely on it initially to give them a quick overview of the proposal and later to refresh their memory of its main points. Agencies often use the abstract alone in their compilations of research projects funded or in disseminating information about successful projects. Though it appears first, the abstract should be written last, as a concise summary (approximately 200 words) of the proposal. It should

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appear on a page by itself numbered with a small Roman numeral if the proposal has a table of contents and with an Arabic number if it does not. To present the essential meaning of the proposal, the abstract should summarize or at least suggest the answers to all the questions mentioned in the Introduction above, except the one about cost (which is excluded on the grounds that the abstract is subject to a wider public distribution than the rest of the proposal). Certainly the major objectives of the project and the procedures to be followed in meeting these objectives should be mentioned. The abstract speaks for the proposal when it is separated from it, provides the reader with his first impression of the request, and, by acting as a summary, frequently provides him also with his last. Thus it is the most important single element in the proposal.

Table of Contents

Very brief proposals with few sections ordinarily do not need a table of contents; the guiding consideration in this is the reader's convenience. Long and detailed proposals may require, in addition to a table of contents, a list of illustrations (or figures) and a list of tables. If all of these are included, they should follow the order mentioned, and each should be numbered with lower-case Roman numerals. If they are brief, more than one can be put on a single page. The table of contents should list all major parts and divisions (including the abstract, even though it precedes the table of contents). Subdivisions usually need not be listed. Again, the convenience of the reader should be the guiding consideration.

Introduction

The introduction of a proposal should begin with a capsule statement of what is being proposed and then should proceed to introduce the subject to a stranger. You should not assume that your reader is familiar with your subject. Administrators and program officers in sponsoring agencies want to get a general idea of the proposed work before passing the proposal to reviewers who can judge its technical merit. Thus the introduction should be comprehensible to an informed layman. It should give enough background to enable him to place your particular research problem in a context of common knowledge and should show how its solution will advance the field or be important for some other work. Be careful not to overstate, but do not neglect to state very specifically what the importance of your research is. In introducing the research problem, it is sometimes helpful to say what it is not, especially, if it could easily be confused with related work. You may also need to explain the underlying assumption of your research or the hypotheses you will be using. If the detailed exposition of the proposed research will be long or complex, the introduction may well end by specifying the order and arrangement of the sections. Such a preview helps a reviewer begin his reading with an orderly impression of the proposal and the assurance that he can get from it what he needs to know. The general tone of the introduction should reflect a

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sober self-confidence. A touch of enthusiasm is not out of place, but extravagant promises are anathema to most reviewers.

Background Section

This section may not be necessary if the proposal is relatively simple and if the introduction can present the relevant background in a few sentences. If previous or related work must be discussed in some detail, however, or if the literature of the subject must be reviewed, a background or literature review section is desirable. A background discussion of your own previous work usually can be less detailed than the customary "progress report." Here you should not attempt to account for time and money spent on previous grants but rather point your discussion to the proposed new (or continuing) research. Sufficient details should be given in this discussion (1) to make clear what the research problem is and exactly what has been accomplished; (2) to give evidence of your own competence in the field; and (3) to show why the previous work needs to be continued. Some sponsors want to know also who has funded the previous work.

Literature reviews should be selective and critical. Reviewers do not want to read through a voluminous working bibliography; they want to know the especially pertinent works and your evaluation of them. A list of works with no clear evidence that you have studied them and have opinions about them contributes almost nothing to the proposal.

Discussions of work done by others should therefore lead the reader to a clear impression of how you will be building upon what has already been done and how your work differs from theirs. It is important to establish what is original in your approach, what circumstances have changed since related work was done, or what is unique about the time and place of the proposed research.

Description of Proposed Research

The comprehensive explanation of the proposed research is addressed not to laymen but to other specialists in your field. This section, which may need several subsections, is, of course, the heart of the proposal and is the primary concern of the technical reviewers. Research design is a large subject and cannot be covered here, but a few reminders concerning frequently mishandled aspects of proposals may be helpful. Be realistic in designing the program of work. Overly optimistic notions of what the project can accomplish in one, two, or three years or of its effects on the world will only detract from the proposal's chances of being approved. Probably the comment most frequently made by reviewers is that the research plans should be scaled down to a more specific and more manageable project that will permit the approach to be evaluated and that, if successful, will form a sound basis for further work. In other words, your proposal should distinguish clearly between long-range research goals and the short-range objectives for which funding is being sought. Often it is best to begin this

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section with a short series of explicit statements listing each objective, in quantitative terms if possible. If your first year must be spent developing an analytical method or laying groundwork, spell that out as Phase 1. Then at the end of the year you will be able to report that you have accomplished something and are ready to undertake Phase 2. Be explicit about any assumptions or hypotheses the research method rests upon. Be clear about the focus of the research. In defining the limits of the project, especially in exploratory or experimental work, it is helpful to pose the specific question or questions the project is intended to answer. Be as detailed as possible about the schedule of the proposed work. When will the first step be completed? When can subsequent steps be started? What must be done before what else, and what can be done at the same time? For complex projects a calendar detailing the projected sequence and interrelationship of events often gives the sponsor assurance that the investigator is capable of careful step-by-step planning. Be specific about the means of evaluating the data or the conclusions. Try to imagine the questions or objections of a hostile critic and show that the research plan anticipates them. Be certain that the connection between the research objectives and the research method is evident. If a reviewer fails to see this connection, he will probably not give your proposal any further consideration. It is better here to risk stating the obvious than to risk the charge that you have not thought carefully enough about what your particular methods or approach can be expected to demonstrate.

Description of Relevant Institutional Resources

The nature of this section depends on your project, of course, but in general this section details the resources available to the proposed project and, if possible, shows why the sponsor should wish to choose this University and this investigator for this particular research. Some relevant points may be the institution's demonstrated competence in the pertinent research area, its abundance of experts in related areas that may indirectly benefit the project, its supportive services that will directly benefit the project, and its unique or unusual research facilities or instruments available to the project.

References

If a list of references is to be included, it is placed at the end of the text proper and before the sections on personnel and budget. The items should be numbered and should be in the order in which they are first referred to in the text. In contrast to an alphabetical bibliography, authors' names in a list of references should not be reversed. In the text, references to the list can be made in various ways; a simple way is to use a raised number at the appropriate place, like this.1 Such numbers should be placed outside any contiguous marks of punctuation. The style of the bibliographical item itself

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depends on the disciplinary field. The main consideration is consistency; whatever style is chosen should be followed scrupulously throughout.

Personnel Section

This section usually consists of two parts: an explanation of the proposed personnel arrangements and the biographical data sheets for each of the main contributors to the project. The explanation should specify how many persons at what percentage of time and in what academic categories will be participating in the project. If the program is complex and involves people from other departments or colleges, the organization of the staff and the lines of responsibility should be made clear. Any student participation, paid or unpaid, should be mentioned, and the nature of the proposed contribution detailed. If any persons must be hired for the project, say so, and explain why, unless the need for persons not already available within the University is self-evident. The biographical data sheets should follow immediately after the explanatory text of the “personnel" section, unless the agency guidelines specify a different format. For extremely large program proposals with eight or more participants, the data sheets may be given separately in an appendix. All biographical data sheets within the proposal should be in a common format.

Budget Section

Sponsors customarily specify how budgets should be presented and what costs are allowable.

Research Proposals -- The Appendices

Some writers are prone to append peripheral documents of various kinds to their proposals on the theory that the bulk will buttress their case. Reviewers almost never read such appendices, and may resent the padding. The best rule of thumb is: When in doubt, leave it out. Appendices to proposals are occasionally used for letters of endorsement or promises of participation, biographical data sheets (when there are too many--say, eight or more--to be conveniently placed in the "personnel" section), and reprints of relevant articles. If two or more appendices are included in a proposal, they should be designated Appendix A, Appendix B, etc.

4.2.2. Proposals for Academic Programs

It may be that your need is not for a research grant, but for outside sponsorship of an academic program involving a new curriculum, a conference, a summer seminar, or a training activity. If so, once again your best guide in proposal preparation is to consult any guidelines that the sponsoring agency provides. In the event that none is available,

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however, the following outline may be followed. The Introduction, including a clear statement of need, and the Background section, describing the local situation and developmental activities to date, should begin the request. These should be followed by a section entitled Planning. This section details the activities that will occur after the grant is received and before the institution of the new courses, training activities, or seminar. A Program Description should come next. This section lists the courses or instructional sessions to be offered, the interrelationship of parts, and the program leading to certification or a degree. It discusses the students or participants to be selected and served by the program, as well as plans for faculty retreats, negotiation with cooperating institutions, released time to write instructional materials, and so on.

Before concluding with the Institutional Resources, Personnel, and Budget sections, special attention should be given to a section entitled Institutional Commitment. Here the agreements made by various departments and cooperating institutions are clarified, and the willingness of the home institution to carry on the program once it has proven itself is certified. This section is crucial to the success of curriculum development programs because, in contrast to research programs, they have a profound impact on the host institution. Funding agencies need to be reassured that their funds will not be wasted by an institution that has only responded to a funding opportunity without reflecting soberly upon the long-range commitments implied.

4.3. Inquiries to Private Foundations

Proposals to foundations have a better chance of succeeding if they are preceded by an informal contact. This contact is usually a brief (not more than two pages) letter outlining the proposed project, suggesting why the foundation should be interested in it, and requesting an appointment to discuss it in further detail. Such a letter permits an investigator to make inquiries to several foundations at once and gives an interested foundation the chance to offer suggestions before receiving the formal proposal.

The initial letter of inquiry should demonstrate that the investigator is acquainted with the work and purposes of the particular foundation being approached and should point out a clear connection between these and the proposed project. A letter so generally phrased that it could be a form letter is almost certain to be disregarded. An effective letter will discuss the significance or uniqueness of the project: Who will benefit? Who cares about the results? What difference will it make if the project is not funded? It will give enough indication of step-by-step planning to show that the project has been thought through and that pitfalls have been anticipated. It will demonstrate the writer's grasp of the subject and his credentials to undertake the project. It will emphasize at the same time that this is a preliminary inquiry, not a formal proposal, and that the investigator will send further details if the foundation wishes, or, better yet, will visit the foundation to discuss the project in depth. It is unnecessary in the preliminary inquiry to include a detailed budget, although an overall cost estimate should be mentioned.

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4.4. Why Proposals Are Rejected?

Assuming that funds are available, that geographical distribution is not a criterion, and that political considerations are not present, the success of a proposal will depend both on the quality of the project itself and the quality of its presentation in the proposal. Different reviewers, of course, will weigh merits and defects differently but the following list of short-comings is worth pondering.

A. Problem

The problem is not of sufficient importance or is unlikely to produce any new or useful information. The proposed research is based on a hypothesis that rests on insufficient evidence, is doubtful, or is unsound. The problem is more complex than the investigator appears to realize. The problem has only local significance, or is one of production or control, or otherwise fails to fall sufficiently clearly within the general field of health-related research. The problem is scientifically premature and warrants, at most, only a pilot study. The description of the nature of the research and of its significance leaves the proposal nebulous and diffuse and without a clear research aim.

B. Approach

The proposed tests, or methods, or scientific procedures are unsuited to the stated objective. The description of the approach is too nebulous, diffuse, and lacking in clarity to permit adequate evaluation. The overall design of the study has not been carefully thought out. The statistical aspects of the approach have not been given sufficient consideration. The material the investigator proposes to use is unsuited to the objective of the study or is difficult to obtain. The number of observations is unsuitable.

C. Investigator

The investigator does not have adequate experience or training for this research. (32.6) The investigator appears to be unfamiliar with recent pertinent literature or methods. The investigator's previously published work in this field does not inspire confidence.

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The investigator proposes to rely too heavily on insufficiently experienced associates.

SECTION B:

Research in Adama University

In all higher institutions training, research and extension are the most important components of learning-teaching process. The decisive factor in all higher education is an instructor. The experience, capacity and capability of an instructor directly affect the qualities of both teaching and learning processes and hence the qualification of the graduates. A good instructor should be well equipped with both theory and practice. The theoretical background can be developed through intensive reading while the practical one through doing and researching. That is why equal weight should be given to both research and academics. An instructor with no research experience is hard to be branded as a qualified instructor.

Adama University (AU) is under the process of fast transformation based on the framework “Setting up Adama University”, which had been initiated by the renowned German Scientist, Prof. Dr. Dr. Herbert Eichele, and was endorsed by the Ministries of Education and Capacity Building. In line with this framework, Adama University has recently started offering master and Ph.D. degrees by research.

Adama University, in its commitment to ensure and maintain quality post graduate research, uses a rigorous thesis research proposal reviewing and approval processes. The process briefly include (1) reviewing of thesis research proposals by professionals; (2) presenting and defending the proposed thesis by candidates at respective Department Graduate Committee (DGC); (3) Checking the inclusion of comments, suggestions, etc by the respective Schools; and (4) submitting to the office of Research Vice-President for the final approval by the Managing Board.

The great majority of the teaching staff is not participating in the research activities and consultancy services for unidentified reasons. The major reasons may be lack of qualified senior staff, lack of research culture and less attention to its importance. The research activity in AU is based the general purposes to:

identify of research priorities, aligned with governmental policies and matching them with the University internal capabilities; encourage partnerships and multidisciplinary research tradition among the staff; and strengthen the University’s research and consultancy capacity.

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5. Writing, Approval and Defense Examination of M.Sc and Ph.D Thesis Proposals

5.1. General Framework of Writing Thesis Proposal

Cover page

Adama

Co-Advisor ________________

Month Year

Department ________________

Major advisor_______________

MR._______________________

M.Sc (PhD) research proposal By

School of _________________

Title _____________________

Adamd University

Title:-

The title of the thesis should be selected carefully. It should be concise, specific, and descriptive enough to contain key words or phrases indicating the contents of the thesis. List of table List of figures Acronyms and abbreviations Abstract (optional):- ) about half page This appears on the second page after the title. The abstract should reflect the content of the paper. It should not exceed 200 words and must include the reason for the study, objectives, methods used and the expected results. The abstract should be in the same

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font as the text written in smaller size font (11 point) key words (up to five, separated by a comma and in alphabetical order) not reflected in the title of the thesis should be given next to the abstract on a separate line.

1. Introduction:- Maximum 4 pages

This part of the paper should provide background information on the subject, justification or underlying hypothesis for doing work and the major objectives of the research or investigation. Formulate a maximum of four specific objectives.

2. Literature Review

An adequate review of literature, limited to information essential to orient the reader should be provided.

3. Materials and Methods

Under this heading a brief and concise description of the study site (area); the procedures, techniques and experimental designs to be used for the data collection; and the methods of data analysis should be given.

4. Work Plan

5. Logistics

6. References

References are listed alphabetically by the author’s last name. References should be selected based on their relevance. As much as possible recent references should be cited and the numbers kept to a minimum. It is the responsibility of authors to check the accuracy of references. References should be presented in the author-year style thus in the text reference to papers by one or two authors are given as shown in the examples below:

IN the case of Ethiopian names, the author’s given (first) name precedes that of the father’s name, e.g. Solomon Kassa and not Kassam S. Ethiopian names should not be abbreviated.

(Abebe Kebede and Ketema Hailu 1989) (Hartmann and Kester,1975, andersson et al..) 1993 Darwin and Morgan,1993) chronologically. According to Abebe Kebede and Ketema Hailu (1989),

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For three or more authors, use et al. (no italics) i.e. Abiy Astatke et al. (1989) in the text (but spell out all authors names in the reference list). Examples of acceptable formats for listing references in the reference section are shown below. References should be in smaller font and hanging paragraph (i, e only the first line of a paragraph should start flush left while the remaining line are indented) No space between consecutive references.

Journal article

Mahli, S.S Harapiak, J.Nybiorg M. and N.A Flore.1991. Soil chemical properties after long term N fertilization of broom grass nitrogen rate. Communications in soil science and plant analysis 22 1447-1458. Gezahegn Ayele and Tekalign Mamo1995. Determinants of demand for fertilizer in vertisol cropping system in Ethopia. Tropical Agriculture ( Trinidad) 72: 165-169

Book

Chapman, D.H. and P.F. pratt. 1961. Methods of Analysis for soils plants and waters. University of California, Riverside, California.(N.B initials appear before last authors family name).

Chapter in book

Loegering, W.Q.1984 \. Genetics of the pathogen host association pp.165-192. In willian R. Bushnell and Alan P. Roelfs (eds) The cereal rusts, Vol 1. Academic press Orlando Florida.

Paper in proceedings

Mesfin Abebe.1992. An investigation into the cause of wilt in cotton. pp. 129-139. In : proceedings of symposium on cotton production under irrigation in Ethiopia Melka Werer, Ethiopia 21-22 October 1982, Institute of Agricultural Research Addis Ababa Provide full names of periodicals in the reference list do not abbreviate.

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Unpublished materials

Citation of unpublished and other source materials not readily available in libraries should not be included in the reference list but should be mentioned in parentheses in the text or as a footnote.

Typing and paper size

The thesis must be typed, double spaced on one side of an A4 sheet (21 by 29.7 cm)

Margins and page numbers

Leave margins of 2.5 cm at the top and sides and 4cm at the bottom of each page. Number all pages.

Headings

Main text headings (A- level) should be centered and typed in bold capitals. Major side headings (B-level) should be bold lowercase letters. Sub headings (C- level) should be light font, lower case letters. Minor sub headings (D-level) should be light font italics.

Table

Table should facilitate comparisons, reveal relationships and save space. Do not repeat information in the text presented in the tables or in charts or graphs. Tables should be numbered consecutively as Table 1, Table 2, etc. in the order in which they are first cited in the text. Each table with its heading should be typed using a smaller font than the text (11 point, similar to the abstract to references). Column heads should be light, not bold.

Units

All measurements are to be reported in SI units. For example, do not use quintal (q) but use Kg or t instead. Units should be written leaving one space after the figures, e.g 2 kg, 3 m, 6.2 cm etc.

Papers based on theses

Papers based on theses should be presented with the thesis adviser(s) and co-author(s), and should indicate the institution, the year the work was done, and the full title of the thesis.

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5. 2. Theses Research Approval Processes

In this approval process, six important steps: namely formulation, submission, reviewing, presentation, correcting presented proposal and final approval are described. 1. Formulation: The postgraduate student in close association with his/her thesis

research advisor(s) should identify researchable topic. Advisors are expected to critically assess thesis research proposal before the student submit to the department for reviewing. The assessment includes every section of the proposal such as the title, relevance of the problem it is addressing, objectives set to address the problem, relevant literature review, materials methods or approaches used to meet the objectives set appropriateness of data analysis method to be used, work plan, the project cost, budget source reference.

2. Submission:- student should submit three hard copies signed by him/her and the advisor(s) of the research proposals written in accordance of the thesis research proposal writing guidelines set by the office of RVP to their respective department head (DGC chairperson) at least ten days before presentation at DGC level.

3. Reviewing:-The DGC chairperson gives the submitted thesis proposal in writings to three professionals to review and tell their comments suggestion and questions during the proposal presentation by the student at the DGC. The main objective of the reviewing is to make the proposal improved, relevant to the standard, and feasible.

4. Presentation: the presentation is a teaching learning processes where the student will have an opportunity to let others know what he/she plans to research and why. The presentation is not aimed to examine the student. It is rather to improve the proposal, teach, help and encourage the student. The DGC chairperson sets a schedule for the student where and when to present his/her proposal. The chairperson has also a responsibility to facilitate the required resource materials (computer, overhead, projector, etc) to be used by the student for the presentation. Advisor(s) and at least two of the reviewers of the proposal should attend the presentation. The reviewers could submit their comments to DGC chairperson or come with their comments to the presentation. The student will present his/her proposal briefly for 15 - 20 minutes. The presentation includes title; brief introduction and objectives; materials and methods (experimental site, experiment descriptions, data collection and analysis); work plan; and logistics (budget breakdown and summary). After the presentation, questions, comments suggestions, and answers will be entertained for about 30 minutes. Priority to comment will be given for the reviewers of the proposals.

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5. Correcting the presented proposal: The student in association with his/her advisor(s) should include all agreed corrections and suggestions during the presentation at the DGC level. The corrected version of the proposal should be signed by the student and his/her advisors) and submitted in five copies to the DGC chairperson. The chairperson in association with the DGC secretary thoroughly checks for the inclusion of the agreed corrections and suggestions. The DGC chairperson should sign on the seven copies of the corrected and accepted proposal and send to the respective School in writing with approved minutes attached.

6. Final Approval: The School assesses the proposal in terms of the guidelines used and the correctness of the procedures used. The Dean signs on complete proposals, and sends to the Research Vice-President for the final approval by the Managing Board, where the logistic is critical examined. Incomplete proposals shall be sent back to respective department for correction and completeness. The completed proposal shall be sealed and sent to the sponsor (i.e. AU/RVP), with a copy to advisor(s), department, Knowledge and Technology Interchange (KTI) unit and the student. The schools will be briefed about the status of the students concerning their thesis proposal, who are involved in advising them, their sponsors and the same will be documented in School’s minutes.

5.3. Procedures and Decision Guidelines on M.Sc. Thesis

Defense Examination

All master (M.Sc. and Med) degree graduate study programs require an original research based thesis. The thesis prepared in 5-6 copies is presented to board of examiners appointed by the School Founder Dean and composed of a chairperson, advisor(s), internal and external examiners. All members of the board of examiners evaluate all aspects of the thesis (title, abstract, introduction, literature review, materials and methods, results discussion, summary and conclusion).

5.3.1. Procedures

The following salient points constitute the procedural guidelines to be followed in the administration of the thesis open defense examination. 1. The thesis defense is open to all interested.

2. The Board of examiners, which is nominated by the department graduate committee (DGC) and endorsed by the School Dean, will examine the candidate.

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3. The chairperson of the board of examiners opens the meeting by introducing the members of the board and inviting the advisor to introduce the candidate and her/his graduate work

4. The advisor introduces and invites the candidate to present his thesis research work.

5. The candidate presents for 20-30 minutes the main results of her/his research work.

6. The members of the board of examiners examine the candidate for about 30-45 minutes on the subject of her/his thesis.

7. About 5 minutes are given to the audience to give comments and ask questions.

8. Based on the result of the open defense examination and assessment of the thesis write up by each member of the board of examiners, an evaluation PASS /FAIL will be given in both the thesis defense evaluation and the performance certification forms, which are accordingly signed by the members.

9. An average letter grade of B or above B denotes Pass and an average letter grade of below B denotes fail. Thesis defense examination grades of B and B+ are equivalent of Good, A is very good and A+ is an excellent.

10. The chairperson announces the decision of the board of examiners to the candidate and the audience.

5.3.2. Decision

The decision of the board of examiners is based on the thesis write up, presentation and the defense examination. The following six decisions are open to the board of examiners.

1. Accepted

Thesis may or may not require typographical and/or minor editorial corrections to be made to the satisfaction of the advisor(s).

2. Accepted with minor modifications

Thesis requires major editorial changes which are to be made to the satisfaction of committee designated by the board of examiners from among its members and the advisor(s). The examining Board’s report must include a brief outline of the nature of the changes required and must indicate the time by which the changes are to be completed.

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3. Accepted with major modification

Thesis requires minor changes in substance and major editorial changes, which are to be made to the satisfaction of committee designated by the board of examiners from among its members, and at least one of the examiners should be a member. The examining board’s report must include a brief outline of the nature of the changes required and must indicate the time by which the changes are to be completed.

4. Deferred

Thesis requires modification of substantial nature the need for which makes the acceptability of the thesis questionable. The examining board’s report must contain brief outline of the modification expected and should indicate the time by which the changes are to be completed. The revised thesis must be resubmitted to the School Dean for re-examination. The reexamination will follow the sane procedures as for the initial submission except that the display period may be reduced or eliminated at the discretion of the School Dean. Normally the same board of examiners will serve. A decision to defer is open only once for each candidate.

4. Pending

If the board is not prepared to reach a decision concerning the thesis at the time of the thesis defense, it is the responsibility of the chair person to determine and obtain what additional information is required by board to reach a decisions and to call another meeting of the board as soon as the required information is revived. Candidates should not normally be required to present themselves to the board of examiners at the second meeting. If all but one member of the board agrees on a decision, the decision shall be that of the majority except when the one dissenting vote is that of the external examiner. In this case the occurrence must be reported to Research Vice-President through the School Dean. If two or more dissenting votes are recorded, the case must be also referred to the Research Vice-President through the School Dean.

5. Rejection

Thesis may be rejected if it does not maintain the standard due to methodologies used in execution, analysis and interpretation. The board of examiners shall report the reasons for rejection and advice on the future opportunity of the candidate to complete his study

6. Appeal

Candidates have the right to appeal their complaints in writing on their own or with consent of the advisor(s) to the CGS through SGS when thesis is differed or rejected by

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the Board of examiners. The CGS will examine the candidate’s appeal VIS a vis the Board of examiners decision by establishing a committee of professionals and pass a final verdict which will be communicated to the candidate and the Board of examiners.

5.3.3. Graduation

The candidate has to incorporate the necessary corrections into the thesis in accordance with the decisions of the board of examiners. This thesis thus revised shall then be submitted to the DGC. The DGC should make sure that all the relevant requests have been accommodated in the revised version of the thesis. The chairpersons of the DGC may solicit help from any member of the board of examiners in this process. The DGC should formally deliberate on thesis prior to submitting its recommendations to the Dean of the respective School.

On the basis of the board of examiners report and their own records of the candidate’s progress in her/ his assigned program of study, the DGC members decide whether the candidate has fulfilled the requirements for the master degree. If the decision positive, the supporting documentation ( hard and soft copies of final thesis) will be forwarded to the School with the request that she or he is recommend to be awarded the degree. The Dean examines this request in light of the report from the chairperson of the board of examiners, and will present to the Research Vice-President, to recommend to the MB that the candidate be awarded the degree.

5.4. Ph.D. Dissertation Defense Examination procedures

PhD degree requires an original research based dissertation. The dissertation prepared in six copes should be submitted to DGC for approval at least three months before the proposed date of defense examination. Dissertations can be submitted either as monograph or compilation of published articles for the work done from the approved dissertation research proposal and or manuscripts. The DGC proposes board of examiners (BOE). The BOE is composed of a chairperson from AU, an internal examiner from AU and an external examiner outside AU. The external examiner could be from abroad or within Ethiopia. The examiners are expected to be holding a professorial rank or Associate professor or equivalent with long experience having supervised Ph.D. students. The proposed BOE would be approved by the School Graduate Council (SGC). The BOE members will have a copy of the dissertation at least two months before the proposed date of examination. The members including the chairperson evaluate all aspects of the dissertation (abstract, introduction, literature review, materials and methods, results, discussion, summary and conclusion).

5.4.1. Procedure

The following salient points constitute the procedural guidelines to be followed in the administration of the dissertation open defense examination

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1. The dissertation defense examination is open to all interested and it will be for a maximum of three hours.

2. The Head of the department from which the candidate is defending opens the defense examination forum by introducing the members of the BOE (name qualification, field of study, position, and institutional affiliation), the advisors and invites the chairperson to invite the major advisor to introduce the candidate.

3. The major advisor introduces the candidate and his/her research work briefly and then invites the candidate to present her/his dissertation research work.

4. The candidate presents for a maximum of one hour the main findings of her/his research work.

5. The chairperson invites the external and internal examiners to present their evaluation assessment of the dissertation for a maximum of 15 minutes each.

6. The chairperson invites the candidate for the reaction to the evaluation assessment.

7. The chairperson invites the examiners to examine the candidate. This examination would be for a maximum of one hour.

8. About 15 minutes are given to the audience including advisors to give comments and ask question.

9. Based on the result of the assessment of the dissertation write up and open defense examination by the BOE, an overall evaluation PASS/FAIL will be given in both the dissertation defense evaluation and the performance certification forms, which are accordingly signed by the BOE.

10. An average letter grade of B or above denotes PASS and an average letter grade below B denotes FAIL. Dissertation defense examination grade of B and B+ is equivalent to good, A is very good and A+ is an excellent.

11. The chairperson announces the decision of the BOE to the candidate and the audience.

5.4.2. Decision

The decision of the BOE is based on the dissertation write up assessment, presentation and the defense examination. The following decisions are open to BOE. 1 Accept the dissertation is accepted as it is.

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2. Accepted subject to major editorial changes in unpublished articles to the satisfaction of the internal examiner and major advisor

3. Differed: the dissertation requires modification of a substantial nature the need for which makes the acceptability of the dissertation questionable. The BOE’s report must contain a brief outline of the modifications expected and should indicate the time by which the changes are to be completed. The revised dissertation must be resubmitted to the School Graduate Council (SGC) through DGC for reexamination. The re- examination will follow the same procedures as for the initial submission except that the display period may be reduced or cancelled at the discretion of the Dean, SGC. Normally the same BOE will serve. A decision to differ is open only once for each candidate.

4. Pending: If the board is not prepared to reach a decision concerning the dissertation at the time of dissertation defense, it is the responsibility of the chairperson to determine and obtain what additional information is required by the Board to reach a decision and to call another meeting of the board as soon as the required information is received. The candidate should not normally be required to present himself to BOE at the second meeting. If all but one member of the board agrees on a decision, the decision shall be that of the majority except when the one dissenting vote is that of the external examiner. In this case the dissenting vote must be reported to RVP through SGC. If there is no agreement or dissenting vote is from the external examiner, the dean School will report the case to the RVP for deliberation and decision.

5. Rejection: Dissertation may be rejected if it does not maintain the standard due to methodologies used in execution, analysis and interpretation. The BOE shall report the reasons for rejection and advice on the future opportunity of the candidate to complete her/his study.

6. Appeal: A candidate has the right to appeal in writings on his own or with consent of the advisor(s) to the VPR through the SGC when the dissertation is differed or rejected by the BOE within one month after the defense examination. The RVP will examine the candidates appeal vis a vis the BOE decision by establishing a committee of professionals and pass a final verdict which will be communicated to the candidate and the BOE within three months of appeal by the student.

5.5. Thesis Advisor’s Remuneration Scheme

1. An M.Sc. thesis research sole advisor both from AU and outside will be paid a remuneration amounting to 6000.00 (six thousand) Birr on successful completion of advisory service evidenced at the end of thesis defense examination. When a co-

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advisor is involved the major advisor will be remunerated 4000 (67% of 6000) Birr and the co-advisor birr 2000 (33% of 6000).

2. The proposed remuneration rate is based on the current consideration of a thesis advisor ship load as 1 credit which equals about 32 credit hr.

3. Students are advised to include thesis advisors remuneration cost in their thesis proposal. Sponsors should pay such cost directly to AU and there should not be any deal about the payment between the student and advisors.

4. The advisor(s) will not have any role in selecting the board of examiners and will not be members of the examining board. The department graduate committee (DGC) is expected to appoint the right professionals to examine the thesis. The procedures of appointing board of examiners and thesis defense examination will be prepared and ratified by SGC.

5. At the end of thesis defense examination, if the verdict of the board of examiners is that the thesis is deferred or passed with major modification, because of the content, write-up methodology, analysis, or interpretation, the advisor (s) are not entitled to the remuneration proposed in no 1 above. If the thesis is deferred only due the candidate’s incompetence to defend the thesis the advisor (s) shall be remunerated.

6. The role of thesis advisor (s) during the thesis defense examination would be limited to introducing the candidate and explaining some issues when requested by the examiners. The advisor(s) may ask the student on certain issues, but should not behave in an offending manner by arguing with other members of the board. The advisor(s) will be in the audience and will not have any role in the process of verdict about the thesis defense. This is to exclude that advisors should not argue on behalf of their advisees and influence the examining board during passing verdict.

7. For those advisors who fail to discharge their duties up to standard, the DGC are commissioned to suggest possible measures.

8. Advisor(s) shall be paid full remuneration for re-defended and qualified thesis which has been deferred on first defense examination.

6. Initiation, Submission and Approval of Research Proposals

Research proposals may be initiated by any university member(s) individually or in group (Fig. 1) and be submitted to the respective department head. The department head is responsible for the facilitation of research activities in the department. He should clearly convince the teaching staff members that there will be no academic promotion without research project and publication. The department head will arrange

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Departmental Annual Research Review Day on which all new research proposals are presented for further amendments, comments, adjustments, etc or even for rejection. Those amendments, comments and adjustments are incorporated in the qualified research proposals and be sent to the Knowledge and Technology Transfer Unit (KTI). The KTI will arrange University Annual Research Review Day, on which new, on-going and completed researches are presented. Internal and external experts are invited for critical evaluation of the research proposal. The KTI will form an ad hoc committee consisting of senior researchers, which will evaluate the new research proposals on the date of the presentation. For this purpose, evaluation sheet will be distributed to the evaluators on that date. The results of the evaluation will be collected by KTI. The originators of the successful research proposals are communicated to incorporate the constructive amendments, comments, adjustments, etc given on the Annual Review Day. The enriched research proposals will be resubmitted to the office of KTI unit in two copies. The KTI office will submit to the office of the Research Vice-President for further endorsement by the Managing Board. The Managing Board can reject, make some financial modifications or fully accept as it is. After getting the approval of the Managing Board, the principal investigator will be communicated through KTI office to submit four copies of the final draft of the proposal and sign contractual agreement with the University (VPR). Finally, the archive, the principal investigator, the KTI and the RVP will each get one copy of sealed research document.

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Research Proposal

Research Proposal (student) Research Proposal (Staff)

Fig.1. Research application procedures

References

Bauer, Henry H., Scientific Literacy and the Myth of the Scientific Method, University of Illinois Press, Champaign, IL, 1992

Bernstein, Richard J., Beyond Objectivism and Relativism: Science, Hermeneutics, and Praxis, University of Pennsylvania Press, Philadelphia, PA, 1983.

Bozinovski, Stevo, Consequence Driven Systems: Teaching, Learning, and Self-Learning Agents, GOCMAR Publishers, Bitola, Macedonia, 1991.

Brody, Baruch A., and Grandy, Richard E., Readings in the Philosophy of Science, 2nd edition, Prentice Hall, Englewood Cliffs, NJ, 1989.

Brody, Thomas A. (1993), The Philosophy Behind Physics, Springer Verlag, ISBN 0-387-55914-0 . (Luis De La Pena and Peter E. Hodgson, eds.) Burks, Arthur W.,

KTI

Department Head

VPR

MB

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Chance, Cause, Reason — An Inquiry into the Nature of Scientific Evidence, University of Chicago Press, Chicago, IL, 1977.

Bynum, W.F.; Porter, Roy (2005), Oxford Dictionary of Scientific Quotations, Oxford, ISBN 0-19-858409-1 .

Chomsky, Noam, Reflections on Language, Pantheon Books, New York, NY, 1975.

di Francia, G. Toraldo (1981), The Investigation of the Physical World, Cambridge University Press, ISBN 0-521-29925-X .

Earman, John (ed.), Inference, Explanation, and Other Frustrations: Essays in the Philosophy of Science, University of California Press, Berkeley & Los Angeles, CA, 1992.

Fleck, Ludwik (1975), Genesis and Development of a Scientific Fact, Univ. of Chicago, ISBN 0-226-25325-2 . (written in German, 1935, Entstehung und Entwickelung einer wissenschaftlichen Tatsache: Einführung in die Lehre vom Denkstil und Denkkollectiv)

Gadamer, Hans-Georg, Reason in the Age of Science, Frederick G. Lawrence (trans.), MIT Press, Cambridge, MA, 1981.

Giere, Ronald N. (ed.), Cognitive Models of Science, vol. 15 in 'Minnesota Studies in the Philosophy of Science', University of Minnesota Press, Minneapolis, MN, 1992.

Glen, William (ed.) (1994), The Mass-Extinction Debates: How Science Works in a Crisis, Stanford, CA: Stanford University Press, ISBN 0-8047-2285-4 .

Godfrey-Smith, Peter (2003), Theory and Reality: An introduction to the philosophy of science, University of Chicago Press, ISBN 0-226-30063-3.

Gauch, Hugh G., Jr., Scientific Method in Practice (2003), Cambridge University Press, 2003, ISBN 0521017084, 435 pages

Hacking, Ian, Representing and Intervening, Introductory Topics in the Philosophy of Natural Science, Cambridge University Press, Cambridge, UK, 1983.

Kuhn, Thomas S., The Structure of Scientific Revolutions, University of Chicago Press, Chicago, IL, 1962. 2nd edition 1970. 3rd edition 1996.

Maxwell, Nicholas, The Comprehensibility of the Universe: A New Conception of Science, Oxford University Press, Oxford, 1998. Paperback 2003.

McComas, William F., ed. The Principal Elements of the Nature of Science: Dispelling the MythsPDF (189 KiB), from The Nature of Science in Science Education, pp53-70, Kluwer Academic Publishers, Netherlands 1998.

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McElheny, Victor K. (2004), Watson & DNA: Making a scientific revolution, Basic Books, ISBN 0-7382-0866-3 .

Mill, John Stuart, "A System of Logic", University Press of the Pacific, Honolulu, 2002, ISBN 1-4102-0252-6.

Newell, Allen, Unified Theories of Cognition, Harvard University Press, Cambridge, MA, 1990.

Ørsted, Hans Christian (1997), Selected Scientific Works of Hans Christian Ørsted, Princeton, ISBN 0-691-04334-5 . Translated to English by Karen Jelved, Andrew D. Jackson, and Ole Knudsen, (translators 1997).

Peirce, C.S. (1998), The Essential Peirce, Selected Philosophical Writings, Bloomington, IN: Indiana University Press , Peirce Edition Project (eds.), Volume 1 (1867–1893) is ISBN 0-253-32849-7, Volume 2 (1893–1913) is ISBN 0-253-33397-0

Salmon, Wesley C. (1990), Four Decades of Scientific Explanation, University of Minnesota Press, Minneapolis.

Shimony, Abner, Search for a Naturalistic World View: Vol. 1, Scientific Method and Epistemology, Vol. 2, Natural Science and Metaphysics, Cambridge University Press, Cambridge, UK, 1993.

Ziman, John (2000). Real Science: what it is, and what it means. Cambridge, Uk: Cambridge University Press.

List of Appendices

Appendix A: Guidelines for reviewers Appendix B: Adama University’s research fund grant agreement form Appendix C: Activity plan and financial request form for release of the first tranche Appendix D: Expenditure report and request form for additional payment Appendix E: Form for thesis research proposal work plan and budget items

Appendix A

Guidelines for Reviewers

Adama University has already started Master and Ph. D by research for up grading of the teaching staff based on the Framework “Setting up Adama University”, which was developed by a highly experienced German Scientist, Prof. Dr. Dr. Herbert Eichele,. The

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fund focuses on selected high priority topics within the framework of the AU and national priorities. The University also invites research proposals by university community, which help the transformation process of the University. It relies on peer reviewers to review and rate research proposals and make recommendations that form the basis for final selection of research proposal for funding. In addition the reviewers provide written feed-back for submission by the University to the researcher for each research proposal. To assist in the process of reviewing research proposals, Adama University has prepared guidelines outlined below. In addition a proposal rating sheet has been prepared for qualitative and quantitative assessment of the proposal.

Part A: Guidelines

Reviewers reports

Reviewer’s critiques (excluding reviewer’s name) i.e. Form B could be forwarded to principal investigator to provide him/her with the feedback on the result of the review of the proposal. No material would be included which might allow the applicant to identify the reviewer.

Confidentiality

Adama University receives research proposals in confidence and is responsible for protecting the confidentiality of their contents. For this reason a reviewers is requested to respect this confidence and to refrain from copying, quoting or otherwise using material from the proposal.

Reviewer- Applicant Contact

It is not expected that a reviewer will contact an applicant directly to discuss a research proposal. If significant contact is inevitable or occurs, it should be noted as part of the confidential report ( Form A)

Review and Rating of Proposals

The attached proposal rating sheet should be used for qualitative and quantitative assessment of each proposal using the following descriptive points as a checklist ------ Appendix A continued -----

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I. Content (Scientific soundness, methodology….etc)

Title

Is the title appropriate & clear? Does it reflect the content of the proposal adequately?

Literature Review

Is there adequate information to demonstrate the feasibility of the project? Has the researcher demonstrated awareness of the previous and alternate approaches to the problem identified in the proposal?

Objectives

Are the objectives relevant & Clear? Do they synchronize with the title of the proposal?

Research Design and Methodology

Is the research design strategy or methodology in accordance with acceptable scientific protocols to meet the objectives? Does the work plan/ implementation timetable follow the most logical approach? Is the methodology fully described, suitable and feasible

Dissemination of Information/ Results

Does the proposal show an effective methodology for dissemination of findings to the end users?

II. Relevance

Focus

Is the research project proposal demand driven Is it unique or original? Does it aim at a fuller exploitation of available technologies and how (e.g. How to enhance productivity from existing released technologies etc.)?

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Is the research testing a sound scientific hypothesis, developing a new technology, seeking to improve or document a new technique, technology, or policy?

Contribution

How large is the target group, which will utilize the results or the technology generated? ------ Appendix A continued -----

Results

Will the anticipated results have an impact in AU transformation framework? Is the impact measurable? What is the probability of the success of the project?

III Budget request

Is the budget request appropriate, relevant and realistic for the need of the research project? Is it cost effective Is the benefit anticipated from the project related to the cost of the research?

Review’s Recommendations

For each proposal, the reviewer should summarize the recommendation in terms of the final action that the Managing Board (MB) should consider. The final recommendation should be in one of the following categories as indicated in the proposal rating sheet: Recommended for funding Recommended for funding after minor corrections/ revisions have been made Proposal recommended for revision and resubmission Not recommended

Reviewer’s response

A reviewer’s early response will be greatly appreciated. If for any reason a reviewer cannot assess a proposal, or may not mail or fax an assessment to reach the Research Vice-President (RVP) within the required time, then he/she should return immediately the research proposal to RVP.

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Correspondence

All correspondence should be addressed to Research Vice-President Adama University P.O.Box 1888 E-mail:VPResearch@Adama-university.net Tel. 022 110 00 53 (Direct) Fax 022 110 00 46 Adama ------ Appendix A continued -----

Part B: Proposals rating sheet for AU

Project Code: ___________________________ Qualitative evaluation Quantitative evaluation A.1 Tick(x) the most appropriate rating in the space provided

A. Write the appropriate score( final score in the scale of 100 points)

I. Content (Scientific soundness, methodology, flow…etc) __________highly appropriate __________Reasonably Appropriate __________Inappropriate

I. content ( Scientific soundness methodology, flow …etc) 0-35 points ___________Score

II. Relevance ________Highly relevant ________reasonably relevant ________irrelevant

II Relevance 0-35 points ___________Score

III. Budget request ________highly realistic ________reasonably realistic ________Unrealistic

III. Budget request 0-15 ___________Score

IV. Resource( other than finance) (competence of applicants and availability of support facilities ___________Likely high ___________Average ___________Low

IV Resources ( other than finance) ( Competence of applicant/s and Availability of support Facilities ) 0-15 Score_______________

Total Score : ________________/100 ------ Appendix A continued -----

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Summary recommendation

Recommended Recommended but minor corrections/ revisions to be made Revise and submit Not recommended Reviewer’s Name Signature Date

Form A:

Comments and confidential suggestions for ARF secretariat use only Form B Comments and suggestions for the consumption of principal investigator (PI) to improve the quality of the project proposal according to the guidelines provided.

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Appendix B

Adama University’s research fund grant agreement form

Agreement dated between Adama University (herein after referred to as “AU” address: P. O. Box 1888, Adama, Ethiopia, Telephone 022 110 00 53, Fax 022 110 00 46 on the one part and the principal investigator (name of applicant hereinafter referred to as “PI” Address P.O. Box and telephone

Whereas:

The PI has requested a research grant from AU for the purpose of financing the project entitled and described as per the attached project document; a) The grant is to be administered by AU and AU has agreed to allocate Birr

( )to the PI upon the terms and conditions

hereinafter set forth

Now therefore, the parties hereby agree as follows:

Article 1

Undertaking by the Principal Investigator

The principal investigator shall: Section 1.01: Undertake the project in accordance with the terms and conditions set forth in this agreement

Section 1.02: Undertake the project within ______weeks after the release of the initial research grant.

Section 1.03: Identify another investigator in the proposal in case he fails to carry out the project for any reason.

Section 1.04: Submit quarterly (in applicable) semi annual and completion reports as per Article 5 of this agreement

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Article 2

Amount of Grant

Section 2.01: AU agrees to allocate a sum of Birr ( ) to the PI for the period of ------ Appendix B continued ----- Years commencing this day of Section 2.02: The fund shall be administered through the AU rules and regulations.

Article 3

Accountability of Adama University

Section 3.01: AU shall administer the researcher project and Provide all services and facilities consistent with the terms and Conditions stated in the agreement.

Section 3.02: AU shall be responsible for the proper administration of the fund allocated for the project. Fund disbursements are made in accordance with the project document, Fund disbursement are valid and supported by adequate documentation, An appropriate system of internal control is maintained and can be relied upon. Financial and progress reports are fair and accurately presented and Uncommitted fund is returned to AU at the end of the project life.

Section 3.03: AU shall administer the funds under its financial regulations, rules, practices and procedures

Section 3.04: As part of fulfilling its judiciary responsibility for the management of the allocated resources, AU shall designate authorized officials and provide written certification thereon for Withdrawal from the special account, Requests for advances of project funds, and Requests for AU to disburse project funds directly

Section 3.05: AU shall ensure maintenance of proper accounts and records of the allocated resources for the project to enable the PI to prepare accurate report on the financial status of funds

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Article 4

Disbursement and Accounting of Fund

Section 4.01: The PI will submit a formal request for an advance of payments for the first six months according to the approved work plan and budget using AU form

Section 4.02: Subsequent request for satisfactory & scheduled progress and financial reports and the audit utilization certificates of earlier released funds should accompany release of funds as necessary and appropriate

Section 4.03: The research fund from the AU is subject to auditors. The result will be published and copies will be made available to all stakeholders.

Section 4.04: Unless otherwise agreed by the parties involved, any unutilized balance must be refunded to AU by the end of the project life.

Article 5

Submission of Reports

Section 5.01: The PI shall submit progress and financial reports in 2 copies, every three months and final report one month after the completion of this agreement.

Article 6

Utilization of the Fund

Section 6.01: The fund granted shall be utilized in accordance with the budget break down presented on the project document

Section 6.02: AU’s accounting & procurement procedures will apply for the administration and management of the fund.

Section 6.03: The PI shall not utilize the fund for purposes other than what is stated in the attached project documents

Section 6.04: Unutilized funds shall be either earmarked for the continuation of the same research/project for the next stage of its development with the consent of MB, or shall be returned to the AU.

Section 6.05: After the completion of this agreement, any equipment or materials acquired from this research fund shall be the property of the institute.

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Section 6.06: AU furnishes with a report listing non expendable property purchased during the project period within 30 days following the end of the project

Article 7

Monitoring and Evaluation

Section 7.01: AU shall follow the progress of the research activity and ensure that work schedules the production of targeted outputs and required actions are proceeding according to plan

Section 7.02: A quarter/semi-annual progress report should be submitted by the PI in relation to the objectives set about the schedule of actions, constraints and plans for the next phases of activities. Failure to submit reports will enforce directives to cease expenditure of funds until the report is received.

Section 7.03: All reports would be examined and reviewed for completeness, attachment of required documentation by AU Knowledge and Technology Interchange

(KTI) unit as appropriate and if further actions were required the PI may be asked to provide explanations.

Section 7.04: Reports should be submitted according to the guidelines for performance and progress report formats. A complete final report should be submitted showing results of the undertaking and expected impact.

Article 8

Publication and Ownership of Intellectual Property Right

Section 8.01: AU does not claim rights to any publications, inventions or patents arising out of the project other than due acknowledgement on publications and information on any meaningful applications of the research result.

Article 9

Change in the Project Documents

Section 9.01: Any major change such as change in the objective(s), methodology, work plan, etc. in the project document shall be reported to KTI.

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Article 10

Breach of Agreement

Section 10.01: Utilization of the fund granted partially or wholly for purposes other than what is stated in the project document.

Section 10.02: Failure in the submission of progress, financial, and final reports.

Article 11

Effect of Breach of Agreement

Section 10.01: The PI shall be liable partially or wholly if there exists a breach of agreement pursuant to Article 10 of this agreement.

Section 10.02: The PI shall be liable for non performance of his/her responsibilities stated in this agreement

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Article 12

Effective Date of the Agreement

This agreement shall come into force on the date of its signature this day of For Adama University The PI Name Name

KTI representative Signature Signature Date Date

Witnesses 1. Name Name Research Vice-President Signature Signature 2. Name Signature 3. Name Signature

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Appendix C

Activity plan and financial request form for release of the first tranch No Planned activities Associated

exp. Remark

Req(Birr)

Total

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Appendix D

Expenditure report and request form for additional payment (To be submitted quarterly with certified copies of accounting and progress reports) Project Title

Principal investigator:

Contract No Tranche No

Host institution:

Part A: Expenditure Report of Tranche No .

Months(identify as months 1,2,3………etc) Month Month______ Month______ Total_________1.Wages/fees/allownace 2. Travel a) Domestic i) per -diem ii) transport b) International

3. Equipment 4. Expendable supplies 5.Pistage/Tel.charge 6. Literature 7. Printing/Publishing 8.Seminars/Workshops 9. Maintenance 10 Miscellaneous Total Part B: Budget request form for the next ( ) trenche a) Amount disbursed in the last tranche( Birr):

b) Total expenditure of current report( Birr)

c) Current Balance (a - b) birr

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d) Request for additional payment to cover the following three months:

Month birr

Month birr

Month birr

Total additional payment requested (birr):

e) Amount to be paid (total additional payment requested less balance) (d - c)

Birr ( )

------ Appendix D continued ----- f) Certification

a) I certify that the funds have been used according to the approved work plan and

reporting make as part of accomplishment of the indicated project. Signed Name Date

b) I certify that the funds have been used on eligible items in the approved work

plan and that the funds have been accounted for according to the institutions account system Signed __________________name ________________Date

g) Examination

I have examined the expenditure detailed above and hereby make the following observations and recommendations Observations Recommendation (s) Signed name date

(Accountant AU) H. Authorization According to the observations and recommendation(s) made under(I) I hereby authorize payment of Birr: ( )

Signed: Name Date (AU)

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Signed: Name Date (RVP)

Signed Name Date Finance specialist (AU) ------ Appendix D continued -----

Summary of Financial And activity Report*

Project Code:

Executed Tranche No

Duration of period to Planned activities / associated Exp. Made Milestones Ass. Exp made Milestones Request (birr) Conditions for release (birr) Remark Name of PI: signature date

For: RVP use only Recommendation

a) High performance recommended for release

b) Medium performance but can be released under close supervision

c) Low performance, immediate evaluation is needed before release

d) Poor performance, stop release and report to the MB for termination

Signature Date

Attach details of work plan by month and related expenditure in a separate paper.

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Appendix E

Form for thesis research proposal work plan and budget items

Work Plan

SN Activities Duration Seed preparation 1

2 Land preparation 3 Planting 4 Field supervision and data collection 5 Laboratory data collection 6 Data analysis and thesis write-up 7 Thesis submission The research work plan should include the time schedule for the accomplishment of the major activities and planned in such a manner that the student will be able to complete his MSc or Med studies within the stipulated period of two years.

Logistics:

A. Personnel Expenses

S.N Activity Total

Payment ( birr)

Daily payment Unit No (Birr)

1 Land preparation Man days 2 Seed preparation Man days 3 Planting Man days 3 Bird scaring Man days 4 Guard Man days 5 Data collection Man days 6 Harvesting Man days 7 Field assistants Man days 8 Enumerators Man days 9 Supervision fee Man days Sub total The different field activities enumerated in the above table are expected to be modified depending on the program and the research project. The man days required for each

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activity will depend on the scope of the research project and is variable between projects. However the supervision fee of 6000 birr is common for all graduate research projects. ------ Appendix E continued -----

B. Per diem

SN Person No of days Daily rate Total 1 Student 90 2 Advisor(s) 10 3 Co- advisor 10 4 Driver

Sub total The per diem request by students should not exceed 90 days under any circumstances. The per diem request for major advisor and co advisor need not exceed 10 days each.

C. Transport Expense

No Person Departure No of trips

Cost trip ( Birr)

Total cost ( Birr) Destination

1 Student 2 Advisor 3 Co-advisor

Sub total The expense under this title include that required for the transport cost of advisors to visit the graduate students research site and the transport costs required by the student to travel occasionally between the research site and Adama University or location of laboratories in which sample analysis are conducted. If vehicle is used for transport, detailed fuel and lubricants requirement to the distance traveled and costs required for the purpose should be included in the table.

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D. Stationery

SN Item Total Price Unit price Unit Quantity (Birr) (Birr)

1 Floppy Dist 1.5 Packet 1 2 Re-writeable CD disk No 1 3 Flash Disk No 1 500 50 4 LaserJet cartridge No 1 1000 1000 5 Printing paper Packet 3 6 Photocopy paper Packet 2 7 Note book ( big) No 2 8 Note book (small) No 2 9 Marker Packet 1 10 Transparency Packet 1 11 Pen Packet 1 12 Staples Packet 2 13 Scotch tape No 1 14 Box file No 2 15 File with fastener No 10 Sub-total Graduate students undertaking field survey as a part of their research can request additional four packets of duplicating paper. The costs for purchasing the flash disk and toner cartridge should not exceed that indicated on the table. The quantities of materials that have to be approved as per the graduate students request should not exceed than what is given in the table.

E. Supplies

SN Items Unit Unit price (Birr)

Total price Quantity

( Birr ) 1 Cloth bag (Abujedi) 2 Chemical Fungicides 3 Paper bag 4 Fertilizer (urea) 5 Fertilizer ( DAP) packet 2 6 Color film Sub total

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The list of materials under this category cannot be complete since it differs between graduate studies program and the nature of research conducted in each program. The department graduate committee (DGC) is expected to stream line the materials and associated costs.

F. Laboratory analysis cost

Type of analysis Total sample Unit cost sample Total Cost Birr

DM Ash ADF NDF ADL CP IVDMD subtotal The items under this cost title are variable between programs and research projects. Each department DGC is expected to evaluate the laboratory costs objectively. Students are expected to undertake all the laboratory analytical work arising from the conduct of their research unless facilities are limiting in the department.

G. Miscellaneous

SN Item or service required Price ( Birr) 1 Film processing and development 100 2 Communications and photocopy services 1000 Subtotal The costs given in the above list are the maximum that can be approved for each item.

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H. Budget summary

No Descriptions Sub total(Birr) 1 Personnel expense 2 Perdiem 3 Travel expense 4 Stationery 5 Supplies 6 Laboratory cost 7 Miscellaneous 8 Contingency (1-5%)

Total 28,000

Budget source: Indicate the sponsor who will cover the cost of the research project.

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