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Part 1 research and evaluation edited

  1. 1 Debre Tabor University Faculty of Business and Economics Department of Economics Research and Evaluation Methods By: Yismaw Ayelign (PhD candidate) September 2, 2017
  2. Cr.hrs: 3 Terget group: 1st Year M.Sc. in Development Economics Students (extension) Objectives  After the completion of this course, students are expected to:  Show their ability to effectively gather, manage, analyze relevant data in support of their written argument;  Show that they have followed good academic research practice and have achieved a good level of competence in academic writing; and  Get basic tools of impact evaluation of public/private interventions. 2
  3. course contents  The course has two parts  The research methodology component comprising of: 1. Brief review of research design (8hrs)  Brief review of contents of the proposal  The literature review  The Qualitative research–Quantitative research debate 2. Techniques of sampling, data collection & analysis (10 hrs)  Sampling  Techniques of data collection  Knowing your data: Data exploration and management  Data analysis tools 3
  4. Outlines 3. Closure of the research process (6hrs)  Types of reports  Components of the research report  Academic writing skills  Writing procedures and findings, writing conclusions. ii. Impact evaluation 4. Introduction to impact evaluation (12 hrs)  Introduction  Exogeneity  Randomization  The counterfactual 5. Introduction to Impact evaluation techniques (14hrs)  Difference-in-difference  Propensity score matching  Instrumental variable methods 4
  5. Methods of Assessment  Assignment-1 (Paper or research proposal with presentation individually). It will be evaluated at 40%  Planned submission deadline 4th week of September, 2017  Assignment-2 : Article review on impact evaluation. It will be evaluated at 20%  planned submission deadline 2nd week of October, 2017.  Final exam :- out of 40% including all chapters  Date: as per the schedule of the department 5
  6. References  Bhattacherjee, Anol (2012). Social Science Research: Principles, Methods and Practices, 2nd ed.  Creswell, John (2003). Research Design: Qualitative, Quantitative, and mixed methods approaches, 2nd ed.  Deaton, Angus (2010). Instruments, Randomization, and Learning about Development, Journal of Economic Literature 48: 424–455.  Ethridge, D.E.(2004).Research Methodology in Applied Economics. Blackwell.  Lawrence Neuman (2000). Social Research Methods: Qualitative and Quantitative Approachs, 4th edn., Allyn and Bacon, Boston. 6
  7.  Matthew B M and Michael A H (1994). Qualitative Data Analysis: An Expanded Source Book, 2nd edn. Sage Publishers,Inc. California.  Ravallion , Martin (nd). The Mystery of the Vanishing Benefits: Ms Speedy Analyst’s Introduction to Evaluation, World Bank.  Rodney and Roberts (2002). Contemporary Social Research Methods, 3rd edn, Wadsworth/Thomson, Belmont.  Walliman, Nicholas (2006). Social Research Methods. 7
  8. Introduction  What is research?  Research is an unbiased, structured and sequential methods of enquiry, directed towards a clear implicit/ explicit objective. This enquiry might lead to validating the existing axioms or arriving at new theories and model. 8
  9. Introduction  What is Scientific research?  “scientific research” satisfies 1) it contributes to a body of science, and 2) it follows the scientific method.  What is science?  Science refers to a systematic and organized body of knowledge in any area of inquiry that is acquired using “the scientific method” 9
  10.  The purpose of science is to create scientific knowledge. Scientific knowledge refers to a generalized body of laws and theories to explain a phenomenon or behavior of interest that are acquired using the scientific method.  Laws are observed patterns of phenomena or behaviors, while theories are systematic explanations of the underlying phenomenon or behavior.  We arrive at scientific laws or theories through a process of logic and evidence. Logic (theory) and evidence (observations) are the two, and only two, pillars upon which scientific knowledge is based. 10
  11. Introduction continued  What is scientific method? It refers to a standardized set of techniques for building scientific knowledge, such as how to make valid observations, how to interpret results, and how to generalize those results. The scientific method allows researchers to independently and impartially test preexisting theories and prior findings, and subject them to open debate, modifications, or enhancements.11
  12. NB: that the scientific method operates primarily at the empirical level of research, i.e., how to make observations and analyze and interpret these observations. Before conducting a scientific research you are expected to select a problem in the form of topic and prepare a plan of action (proposal). Before the proposal the research problem is chosen in the form of a research topic. 12
  13. A research topic/problem/question/ agenda Sources of research topic A) Professional Experience  Own professional experience is the most important source of a research problem.  Many researchers are directly engaged in program implementation and come up with a topic based on what they see is happening around them.  Contacts and discussions with others,  attending conferences, seminars, and listening to professional speakers are all helpful in identifying research problems. 13
  14. Topic --- b) Inferences from the literature  Another source for research ideas is the theoretical or empirical literature in your specific field.  Many researchers get ideas for research by reading the literature.  Two types of literature can be reviewed.  The conceptual literature  The empirical literature  Research reports, bibliographies of books, and articles, periodicals, research abstracts and research guides suggest areas that need research. 14
  15. Topic --- C) Provided by a client  Requests For Proposals (RFPs) are published by government agencies and some companies.  These RFPs describe some problem that the agency would like researchers to address -- they are virtually handing the researcher an idea.  The RFP describes the problem that needs addressing, the contexts in which it operates, the approach they would like you to take to address the problem, and the amount they would be willing to pay for such research. d) Technological and Social Changes  New developments bring forth new development challenges for research. 15
  16. A research topic/problem/question/ agenda A good research topic should satisfy:  Specific (optimally narrow )  interesting and/or important & should match with your professional qualification  clearly stated (not vague)  has a logical rational and tied to theory and empirical set up  is doable (not over done, not Controversial , etc. ),  The choice of a research topic can be influenced by various factors such as: 16
  17.  your background, education, and experience,  culture, Interest and Values of the Researcher, Current Debate in the Academic world, Funding, The value and power of the subject, etc.  Many of these involve making tradeoffs between rigor and practicality. 17
  18. Brief contents of a proposal  Introduction 1.1 background :- is a sequential and system build – up to the research problem and also a compelling reason for pursuing the study. Start at broader issue related to your topic End explaining about the problem 18
  19. Proposal contents 1.2. problem statement (knowledge gap identification) 1.3. Objectives 1.4. Hypothesis/research Questions 1.5. Significance of the study/ outcomes and beneficieries 1.6. Scope/delimitation 1.7. Limitation of the study 19
  20. Problem statement  First review the existing theoretical and empirical research and then come up with the knowledge gap that your research result is expected to fill. The gap may be interms of : Methodlogical:- e.g. Maximumlikelihood Vs. OLS, random effect Vs. Fixed effects ; Descriptive Vs. Inferential  Time gap in case if the existing researches are old enough so that they could not tell about the contemporary issue.  Area in case there is supportive evidence that the nature of the problem and its determining factors differ across geographical locations. 20
  21. Problem continued  Nature of data: in recent years panel data is available at microlevel for many problem so if previous studies focused macrodata, you may decide to use microlevel data. Even you may decide to use panel instead of cross-sectional data but with reason/justification.  Unresolved discussion related to the topic (not advisable to beginners) You may be interested to participate in areas such as various researches have been conduted but the results are not consistent. The justifications given are not satisfactory. 21
  22. Objectives:-  This is the step of rephrasing the problem into operational or analytical terms, i.e. to put the problem in as specific terms as possible. In this section the specific activities to be performed are listed.  The general objective provides a short statement of the specific goals pursued by the research.  The specific objectives are the objectives against which the success of the whole research will be judged. 22
  23.  t should be SMART. It has implications to your pcted methodology  It must flow logically and clearly from the purpose, problem statement and justification already stated. It is operational Hypothesis:-  Tentative answer to the research question  how to formulate a hypothesis 1.5. Significance of the study/ outcomes and beneficieries 1.6. Scope/delimitation 1.7. Limitation of the study 23
  24. Literature Review  Involes a comprehensive compilation of the information obtained from both published and unpublished sources which belong to the specific area of interest to the researcher. It has two levels: theoretical level and empirical level. The empirical level deals with testing theoretical concepts and relationships to see how well they match with our observation of reality with the ultimate goal of building better theories.  Surveys all relevant literature to determine what is known and not known about a particular topic. 24
  25. Literature review  The purpose of a literature review 1) to survey the current state of knowledge in the area of inquiry: To discover what has been written about a topic already 2) to identify key authors, articles, theories, and findings in that area: To determine what each source contributes to the topic 25
  26. Literature review continued 3) to identify gaps in knowledge in that research area: To understand the relationship between the various contributions, identify and (if possible) resolve contradictions, and determine gaps or unanswered questions.  How can one conduct literature review? Steps : i. Define and Refine a Topic: you need to begin a literature review with a clearly defined, well-focused research question and a plan. 26
  27. Literature review continued  ii. Design a Search: - plan a search strategy. decide on the type of review, its extensiveness, and the types of materials to include. Collect the materials to be reviewed. And plan your style of citation. iii. Read with purpose: iv. Evaluate the articles for: credibility: is the author an expert in the area?  Argument/Evidence: Does the evidence support the conclusion? Is the argument or evidence complete? 27
  28. Literature review continued  Sources of literature to be reviewed  Researchers can find reports of research studies in several formats: books, scholarly journal articles, dissertations, government documents, and policy reports. Researchers also present findings of papers at the meetings of professional societies. This section discusses each format and provide a simple road map on how to access them. 28
  29. Literature review continued  Outlines of a literature review  Concepts and operational definitions  Theoretical framework  Empirical framework  Critical summary of the literature  Conceptual framework 29
  30. Literature review continued Citation  in text citation  Reference list  Purpose of citation: i. giving guidance to to the reader to get further information (to track down the sources) ii. Acknowledgement (recognition or credit) to authors iii. To avoid plagiarism / acriditation of your work  Various styles of citation are available from which you need to choose e.g. Harvard style, APA style, Oxford style and many institutions have their own based on international standards. 30
  31. Literature review continued  Organization of the reviewed note you need an organizing method. One approach is to group various studies or specific findings by skimming notes and creating a mental map of how they fit together. Organization is skill so it needs practice implying that you have to try alternative patterns of organizing. Choose one or more of the following organizational methods based on the objective of the review. 31
  32.  A context review implies organizing recent reports around a specific research question.  A historical review implies organizing studies by major theme and by the date of publication. An  integrative review implies organizing studies around core common findings of a field and the main hypotheses tested.  A methodological review implies organizing studies by topic and, within each topic, by the design or method used. A theoretical review implies organizing studies by theories 32
  33. Methodology:-  Description of study area  Data type and source  Sampling techique  Sample size determination  Data collection tools  Data analysis Technique 33
  34. Quantitative Qualitative research Debate  Quantitave research is ‘Explaining phenomena by collecting numerical data that are analysed using mathematically based methods (in particular statistics).  Actually, explaining phenomena is a key element of all research, be it quantitative or qualitative. When we set out to do some research, we are always looking to explain something. 34
  35. Quantitative ... continued  In order to be able to use mathematically based methods our data have to be in numerical form. So, particular questions seem immediately suited to being answered using quantitative methods. It can use inferential statistics 35
  36. Quantitative ... continued  When do we use quantitative approach?  When we want a quantitative answer. e.g. How many student choose to study economics?  Numerical change (growth): e.g. Is the GDP growing over time? Is income of households rising? Is productivity of firms growing etc. ?  If we want ant to explain phenomena. The determining factors of an event for its occurring or its absence. e.g. What factors are related to changes in direct investment in Ethiopia? What factors predict the changes in technical efficiency of firms? 36
  37. Quantitative ... continued  What are the determining factors of tourism in Amhara region etc.?  Testing of Hypotheses. We might want to explain something, for example whether there is a relationship between students’ performance and study hour and their social background, family income, etc. 37
  38. Quantitative ... continued  While quantitative methods are good at answering the above four types of questions, there are other types of question that are not well suited to quantitative methods. 1. The first situation where quantitative research will fail is when we want to explore a problem in depth. Quantitative research is good at providing information in breadth from a large number of units. 38
  39. Quantitative ... continued  But when we want to explore a problem or concept in depth quantitative methods are too shallow. To really get under the skin of a phenomenon, we will need to go for ethnographic methods, interviews, in- depth case studies and other qualitative techniques. 2. What quantitative methods cannot do very well is develop hypotheses and theories. The hypotheses to be tested may come from a review of the literature or theory, but can also be developed using exploratory qualitative research. 39
  40. Quantitative ... continued 3. If issues to be studied are particularly complex, an in- depth qualitative study (a case study, for example) is more likely to pick up on this than a quantitative study. This is partly because there is a limit to how many variables can be looked at in any one quantitative study, and partly because in quantitative research it is the researcher who defines the variables to be studied. In qualitative research unexpected variables may emerge. 40
  41. Quantitative ... continued 4. Finally, while quantitative methods are better at looking at cause and effect (causality), qualitative methods are more suited to looking at the meaning of particular events or circumstances. Mixed Research Design What then do we do if we want to look at both breadth and depth, or at both causality and meaning? In these situations, it is best to use mixed methods design in which we use both quantitative (e.g., a questionnaire) and qualitative (e.g., a number of case studies) methods. 41
  42. Quantitative ... continued  Mixed methods research is a flexible approach: qualitative or quantitative components can predominate or both can have equal status. 42
  43. Chapter Two Techniques of Sampling, Data Collection & Analysis  Sampling  Techniques of data collection  Knowing your data: Data exploration and management  Data analysis tools Look at the following diagram adapted from W. Larrence Neuman (2014) 7th ed. p18 43
  44. 44
  45. Research design  After the research problem is well formulated the research is expected to develop a research design. S/he will have to state the conceptual structure within which research would be conducted. The preparation of such a design facilitates research to be as efficient as possible yielding maximal information.  In other words, the function of research design is to provide for the collection of relevant evidence with minimal expenditure of effort, time and money. 45
  46.  But how all these can be achieved depends mainly on the research purpose.  Research purposes may be grouped into four categories, viz., (i) Exploration, (ii) Description, (iii) Diagnosis, and (iv) Experimentation.  Research design is the blue print or master plan  A flexible research design which provides opportunity for considering many different aspects of a problem is considered appropriate if the purpose of the research study is that of exploration. 46
  47.  It should be designed in such a way that minimises bias and maximises the reliability of the data collected and analysed.  There are several research designs, such as, experimental and non-experimental hypothesis testing. Experimental designs can be either informal designs (such as before- and-after without control, after-only with control, before- and-after with control) or formal designs (such as completely randomized design, randomized block design, Latin square design, simple and complex factorial designs), out of which the researcher must select one for his own project. 47
  48.  Its preparation includes:  the means of obtaining the information = sampling design  the availability and skills of the researcher and his staff (if any);  explanation of the way in which selected means of obtaining information will be organised and the reasoning leading to the selection;  the time available for research; and  the cost factor relating to research, i.e., the finance available for the purpose 48
  49.  Terminologies  Population:- A population can be defined as all people or items (unit of analysis) with the characteristics that one wishes to study. All the items under consideration in any field of inquiry constitute a ‘universe’ or ‘population’.  Population can be finite or infinite  The unit of analysis may be a person, group, organization, country, object, or any other entity that you wish to draw scientific inferences about. 49
  50.  sampling frame is an accessible section of the target population (usually a list with contact information) from where a sample can be drawn.  Sample:- a portion of the population drawn from the population for the sake of observation ( to collect data from).  Data:- are the forms of empirical evidence or information carefully collected according to the rules or procedures of science. 50
  51.  Empirical evidence: - Description of what we can observe and experience directly through human senses (e.g., touch, sight, hearing, smell, taste) or indirectly using techniques that extend the senses.  Data can be numerical (quantitative) and non- numerical (qualitative) information and evidence that have been carefully gathered according to rules or established procedures 51
  52. Sampling Vs. Census  Census: a complete enumeration of all the population-A census is a survey of all of the cases in a population. A complete enumeration of all the items in the ‘population’ is known as a census enquiry. E.g.  Sampling: is the statistical process of selecting a subset (called a “sample”) of a population of interest for purposes of making observations and statistical inferences about that population. 52
  53.  We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It can be presumed that in such an inquiry when all the items are covered no element of chance is left and highest accuracy is obtained. But in practice this may not be true. Even the slightest element of bias in such an inquiry will get larger and larger as the number of observations increases. Moreover, there is no way of checking the element of bias or its extent except through a resurvey or use of sample checks 53
  54.  This type of inquiry involves a great deal of time, money and energy and hence not possible in practice.  We need to select only a few items from the universe for our study purposes. The items so selected constitute what is technically called a sample. The way of selecting a sample is called the sample design. A sample design is a definite plan determined before any data are actually collected for obtaining a sample from a given population.  Steps in sample design:- 54
  55.  Identifying the universe  Sampling unit: Sampling unit may be a geographical one such as state, district, village, etc., or a construction unit such as house, flat, etc., or it may be a social unit such as family, club, school, etc., or it may be an individual.  Source list (sample frame):  Size of sample:-  Parameters of interest:- estimating the proportion of persons with some characteristic in the population, or we may be interested in knowing some average 55
  56. or the other measure concerning the population.  Budgetary constraint:-  Sampling procedure:- he must decide about the technique to be used in selecting the items for the sample. this technique or procedure stands for the sample design itself. S/he must select that design which, for a given sample size and for a given cost, has a smaller sampling error. 56
  57. Criteria of Selecting a Sampling Procedure  Two costs are involved in a sampling analysis viz., the cost of collecting the data and the cost of an incorrect inference resulting from the data.  Two causes of incorrect inferences viz., systematic bias and sampling error. A systematic bias results from errors in the sampling procedures, and it cannot be reduced or eliminated by increasing the sample size. The causes responsible for these errors can be detected and corrected. Usually a systematic bias can result from one or more of the following factors: 57
  58.  Inappropriate sampling frame:  Defective measuring device:  Non-respondents:  Indeterminancy principle: Sometimes we find that individuals act differently when kept under observation than what they do when kept in non-observed situations.  Natural bias in the reporting of data:- Natural bias of respondents in the reporting of data is often the cause of a systematic bias in many inquiries. 58
  59. There is usually a downward bias in the income data collected by government taxation department, whereas we find an upward bias in the income data collected by some social organisation.  Sampling errors are the random variations in the sample estimates around the true population parameters.  It decreases with the increase in the size of the sample, and it happens to be of a smaller magnitude in case of homogeneous population 59
  60.  Sampling error can be measured for a given sample design and size. The measurement of sampling error is usually called the precision of the sampling plan.  If we increase the sample size, the precision can be improved. But increasing the size of the sample has its own limitations viz., a large sized sample increases the cost of collecting data and also enhances the systematic bias. Thus the effective way to increase precision is usually to select a better sampling design which has a smaller sampling error for a given sample size at a given cost. 60
  61.  Generally, while selecting a sampling procedure, researcher must ensure that the procedure causes a relatively small sampling error and helps to control the systematic bias in a better way.  A good sample design is one that produces representative sample (result will be generalizable to the population) with small sampling error and systematic biase is controlled viable to conducted under the resource limit.  On the basis of representation sample design can be either probability samples or non-probability samples. 61
  62.  Probability sampling = random sampling or chance sampling. Items are selected not deliberately but by some mechanical process. It is blind chance alone that determines whether one item or the other is selected.  The results obtained from probability, can measure the errors of estimation or the significance of results obtained from a random sample, and this fact brings out the superiority of random sampling design over the deliberate sampling design. 62
  63.  Random sampling ensures the law of Statistical Regularity which states that if on an average the sample chosen is a random one, the sample will have the same composition and characteristics as the universe. This is the reason why random sampling is considered as the best technique of selecting a representative sample.  With probability samples each element has a known probability of being included in the sample but the non- probability samples do not allow the researcher to determine this probability. 63
  64.  Probability samples are those based on simple random sampling, systematic sampling, stratified sampling, cluster/area sampling  Non-probability samples are those based on convenience sampling, judgement sampling and quota sampling techniques  Convenience sampling: a sample drawn based on the ease of access. Not possible to check representativeness.  A judgement sampling the researcher’s judgement (purposive) is used for selecting items which 64
  65. he considers as representative of the population.  Can be used in qualitative research where the desire happens to be to develop hypotheses rather than to generalise to larger populations.  Quota sampling :- is used regularly by reporters interviewing on the streets.  Snow ball sampling :-  The basic question in sampling is how representative is the information collected of the whole population?  how similar are the characteristics of the small group of cases that are chosen to those of all the cases in the 65
  66.  To be able to make accurate judgements about a population from a sample, the sample should be as representative as possible Reasons for sampling:- i. Cost consideration (economy reason) ii. Some times sample gives accurate result as the population iii. census may be dangerious to the lives of the population iv. Sample is the only choice for infinte population 66
  67.  In probability sampling the first question about the population is whether it is homogenous or not, how are they distributed (e.g. are they grouped in different locations, found at different levels in a hierarchy or are all mixed up together)? Different sampling techniques are appropriate for each.  The next question to ask is: which process of randomization will be used?  Simple random sampling:- you need a list of the population to apply to either random no.s or lottory. 67
  68.  Systematic random sampling:- when the population is very large and of no known characteristics. The first case is picked randomly. E.g. the Kth item. sample is spread more evenly over the entire population.  Disadvantage: -If there is a hidden periodicity in the population, systematic sampling will prove to be an inefficient method of sampling.  Stratified random sampling:- in case if the population fall into distinctly (non overlapping) different categories or strata. How to allocate the sample size in to each stratum? 68
  69.  Proportional Vs. disproportional. Heterogenous population  strata be formed on the basis of common characteristic(s) of the items to be put in each stratum. ensure elements being most homogeneous within each stratum and most heterogeneous between the different strata. How to select a common x/cs? Past experience or pilot study may be conducted for determining a more appropriate and efficient stratification plan. How can noe draw sample from each stratum? Either simple random sampling or systematic sampling, which ever is considered69
  70.  Proportionate stratified sampling:- if known proportion of that population for each category. The sizes of the samples from the different strata are kept proportional to the sizes of the strata. n . Pi. Where Pi the ratio of population of stratum “i” to the total population  Disproportionate: - 1. the purpose happens to be to compare the differences among the strata, then equal sample selection from each stratum would be more efficient even if the strata differ in sizes. 70
  71. 2. In cases where strata differ not only in size but also in variability and it is considered reasonable to take larger samples from the more variable strata. then account for both (differences in stratum size and differences in stratum variability) by using disproportionate sampling design by requiring.  n1/N1s1 = n2 /N2s2 = ......... = nk /Nksk. This is called optimum allocation 71
  72. 3. If differences in stratum size and differences in stratum variability, and differences in stratum sampling cost, then we can have cost optimal disproportionate sampling design by requiring 72
  73.  Cluster sampling  Multistage sampling (sequential sampling) Sample Size Determination Instead of asking about required precision, many people ask, “What percentage of the population should I include in my sample?” This is the wrong question to be asked (Lohr, Sharo L. (2010) Sampling: design and Analysis 2nd ed. CENGAGE Learning. P46). 73
  74. Sample Size Determination  What should be the size of the sample? If the sample size (‘n’) is too small, it may not serve to achieve the objectives and if it is too large, we may incur huge cost and waste resources.  Take an optimum size i.e., it should neither be excessively large nor too small.  It must be chosen by some logical process before sample is taken from the universe. 74
  75. Sample size continued Consider the following when determining sample size: i. Nature of universe: homogenous Vs. Heterogeneous ii. Number of classes proposed: large sample size for large number of groups  Nature of study: If items are to be intensively and continuously studied, the sample should be small. For a general survey the size of the sample should be large, but a small sample is considered appropriate in technical surveys. 75
  76. Sample size continued iv. Type of sampling: A small random sample is much superior to a larger but badly selected sample. v. Standard of accuracy and acceptable confidence level: for higher level of accuracy (degree of precision), we need large sample size.  Who will decide the margin of error and the level of significance (or confidence level)?  Answer: the researcher vi. Availability of finance: 76
  77. Sample size continued vii. Other considerations: Nature of units, size of the population, size of questionnaire, availability of trained investigators, the conditions under which the sample is being conducted, the time available. two alternative approaches for determining the size of the sample 1. specify the precision of estimation desired 2. use Bayesian statistics to weigh the cost of additional information against the expected value of the additional information. 77
  78. Sample size continued  The first approach is capable of giving a mathematical solution, and as such is a frequently used technique of determining ‘n’. The limitation of this technique is that it does not analyse the cost of gathering information vis-a-vis the expected value of information.  The second approach is theoretically optimal, but it is seldom used because of the difficulty involved in measuring the value of information 78
  79. Sample size continued Sample size determination via precision rate and confidence interval  The point is that there is sampling error which can be reduced by taking adequate sample size. So you need to specify the level of precision in estimating a population parameter. e.g. You may assume the population mean lies within +3. If the sample mean is 15, the true value ranges from 12 to 18. thus the acceptable error is 3. 79
  80. a. Sample size to estimate population mean:- for infinite population  where X = sample mean;  z = the value of the standard variate at a given confidence level (to be read from the table giving the areas under normal curve) and it is 1.96 for a 95% confidence level;  n = size of the sample 80 Sample size continued
  81.  = standard deviation of the population (to be estimated from past experience or on the basis of pilot survey). e.g. e= 3, = 4.2, confidence interval = 95% b. Sample size to estimate population mean:- for finite population 81 Sample size continued
  82. Sample size continued  Where is the finite population multiplier  N= population size Home work: Given N= 600, e= 0.8, population variance = 4, at 99% confidence interval (i.e. Z= 2.57). Compare the sample size for finite and infinite population. 82
  83. What if the population standard deviation (variance) is not known?  We can make a rough estimate the standard deviation based on the range (difference between the highest and lowest values) of the population. As you know 99.74% of the population lies within +3, which implies that we can consider 6 standard deviation. e.g. If the given range is 18 then, 83 Sample size continued
  84.  This can be used to determine the sample size based either of the above formulae as the case may be c. Sample size when estimating a percentage or proportion:- For infinite population  The procedure is similar as before: Decide the precision level and confidence interval Since the confidence interval is  where p = sample proportion, q = 1 – p;  z = the value of the standard variate at a given confidence 84 Sample size continued
  85. level and to be worked out from table showing area under Normal Curve;  n = size of sample.  How to get the value of p? A. Pilot survey (225 or abaove) B. Previous studies and expert experiance C. Personal judgement (most conservative case) p=0.5 85 Sample size continued
  86.  For finite population  e.g. A manufacturing firm wants to see the proportion of defective products. The total product is 6000 with in +2% error margin and 95.5% confidence level. Let p =0.03  Compare the sample size for finite and infinite population cases. 86 Sample size continued
  87. Data collection Techniques  Data collection is the process of gathering and measuring information on variables of interest in an accepted and systematic fashion.  Methods of data collection may vary by discipline and data types;  BUT the emphasis on ensuring accurate collection remains the same.  Improper collection of data lead to wrong conclusions 87
  88. Data collection continued  Data types Primary Vs. Secondary; Primary data:- first hand data collected by the researcher. It can be either qualitative or quantitative Secondary data:- those, which have been collected by other individuals or agencies. If a trusted worthy secondary data is available, then why reinvent the wheel if the data already exists. 88
  89. When dealing with secondary data you should ask:  Is the owner of the data making them available to you?  Is it free of charge? If not, how will you pay?  Are the data in a format that you can work with?  A description of the sampling technique, i.e., how the sample was collected is also necessary. 89 Data collection continued
  90. Data Collection Techniques Sources of Secondary Data  Secondary data may be acquired from various sources:  Documents (reports of various kinds, books, periodicals, reference books (encyclopedia), university publications (thesis, dissertations, etc.), policy documents, statistical compilations, proceedings, personal documents (historical documents, Data archives, etc.  The Internet e.g. websites of IMF, UNDP, WB, etc.
  91. Advantages of Secondary data  Can be found more quickly and cheaply.  Most researches on past events or distant places have to rely on secondary data sources. e.g. Time series data Limitations  Authenticity:  genuine?  credible? Data Collection Techniques
  92.  Representative?  The issue of completeness  The information often does not meet one’s specific needs.  Definitions might differ, units of measurements may be different and different time periods may be involved.  Data could also be out of date. Data Collection Techniques
  93. Primary Sources of Data  Data that came into being for the first time by the people directly involved in the research.  There are two approaches to primary data collection:  the qualitative approach and  the quantitative approach  Qualitative data can be acquired from:  case studies,  focus group discussions and key informant interviews. Data Collection Techniques
  94. i) Case studies  A case study research involves a detailed investigation of a particular case. Through Interviews or Through Direct observation (field visits). ii) Focus group discussions A FGD is a group discussion guided by a facilitator, during which group members talk freely and spontaneously about a certain topic. Data Collection Techniques
  95.  The group of individuals are expected to have experience or opinion on the topic and are selected by the researcher. Its purpose is to obtain in-depth information on concepts, perceptions and ideas of a group. It is more than a question-answer interaction.  group members discuss the topic and interact among themselves with guidance from the facilitator. Why use focus groups?
  96.  The main purpose of a focus group research is to draw upon respondents’ attitudes, feelings, beliefs, experiences and reactions.  attitudes, feelings and beliefs may likely be revealed via interaction in social gatherings.  Compared to individual interviews, which aim to obtain individual attitudes, beliefs and feelings, focus groups elicit a multiplicity of views and emotional processes within a group context.
  97. Strengths and weakness of FGDs  FGDs can be a powerful research tools which provide valuable information in a short period of time and at relatively low cost if the groups have been well chosen, in terms of composition and number.  BUT, FGD should not be used for quantitative purposes, such as the testing of hypotheses or the generalization of findings for larger areas, which would require more elaborate surveys.
  98.  In addition, it may be risky to use FGDs as a single tool.  b/c in group discussions, people tend to centre their opinions on the most common ones.  b/c in case of very sensitive topics group members may hesitate to express their feelings and experiences freely.  Therefore, it is advisable to combine FGDs with other methods (in-depth interviews). iv) Key Informant Interview  an interviewing process for gathering information from opinion leaders such as elected officials, government officials, and business leaders, etc.
  99.  This technique is particularly useful for:  Raising community awareness about socio-economic issues  Learning minority viewpoints  Gaining a deeper understanding of opinions and perceptions, etc. v) Triangulation  refers to the use of more than one approach to the investigation of a research question in order to enhance confidence in the findings.
  100.  The purpose of triangulation is to obtain confirmation of findings through convergence of different perspectives. Why use triangulation  By combining multiple methods, and empirical materials, researchers can hope to overcome the weakness or biases and problems that are associated with a single method.  Taxonomy of triangulation  Data triangulation:  Investigator triangulation:  Theoretical triangulation:-  Methodological triangulation:
  101. Quantitative Primary Data Collection Methods This method involves numeric or statistical approach. It involves the collection of quantified data that can be subjected to statistical treatment. Primary data may be collected through:  Direct personal observation method, or  Survey or questioning other persons,  From a literature search, or by combining them.
  102. The Observation Method  Observation includes all kinds of monitoring behavioral and non-behavioral activities.  Advantages  It is less demanding and has less bias.  One can collect data at the time it occurs and need not depend on reports by others.  with this method one can capture the whole event as it occurs.
  103. Weakness of the Method  The observer normally must be at the scene of the event when it takes place.  But it is often difficult or impossible to predict when and where an event will occur.  It is also a slow and expensive process.  Its most reliable results are restricted to data that can be determined by an open or surface indicator.  Difficult to learn about past events and to gather information on intensions, attitudes, opinions and preferences.
  104. The Survey Method: the most commonly used method in economics.  To survey is to ask respondents questions in a questionnaire - mailed or handled by interviewers. Strength of the Survey Method  It is a versatile or flexible method - capable of many different uses.  More efficient and economical than observations - surveying using telephone or mail is less expensive.
  105. Weakness of the Method  The quality of information depends heavily on the ability and willingness of the respondents.  A respondent may interpret questions or concept differently from what was intended by the researcher.  A respondent may deliberately mislead the researcher by giving false information.
  106.  Surveys could be carried out through:  Face to face personal interview  By telephone interview  By mail or e-mail, or  By a combination of all these. a) Personal Face to face Interview  It is a two-way conversion where one person interviews another person for detailed information.
  107. Advantages The depth and detail of the information that can be secured far exceeds the information secured from telephone or mail surveys. Interviewers can probe additional questions, gather supplemental information through observation, etc. Interviewers can make adjustments to the language of the interview because they can observe the problems and effects with which the interviewer is faced.
  108. Limitations of the Method The method is an expensive enterprise – usually US$50+ per interview now (e.g., locating respondents) Hence, personal interviews are generally used only when subjects are not likely to respond to other survey methods. susceptible to interviewers’ bias/mistakes Interviewer may also be reluctant to visit unfamiliar places.
  109. b) Telephone Interview  Telephone can be a helpful medium of communication in setting up interviews.  Telephone surveys are the fastest method of gathering information from a relatively large sample.  Telephone surveys are generally short and last less than ten minutes.
  110. Strengths of this method  Moderate travel and administrative costs  Faster completion of study  Responses can be directly entered on to the computer Limitations of this method  Respondents must be available by phone.  The length of the interview period is short.  those interviewed by phone find the experience to be less rewarding than a personal interview.
  111. Advantages  Lower cost than personal interview  Persons who might otherwise be inaccessible can be contacted (major corporate executives)  Less interviewer bias  better protects privacy/anonymity C) Interviewing by mail (Solicited responses)  Self-administrated questionnaires may be used in surveys.  They are ideal for large sample sizes, or when the sample comes from a wide geographic area. Disadvantages  Non response error is high  Large amount of information may not be acquired
  112. d) Online Surveys (E-mail and internet)  E-mail surveys are relatively new and little is known about the effect of sampling bias in internet surveys. Advantages:  Very inexpensive -saves inputting costs as well  Respondents feel privacy Disadvantages:  Very biased toward wealthy – e.g. in Ethiopia  even in industrialized world, the very poor have less online access  And biased towards the young So, the demographic profile of the internet user does not always represent the general population.
  113.  Therefore, before doing an e-mail or internet survey, carefully consider the effect that this bias might have on the results. Questionnaire Design The instrument design begins by drafting specific measurement questions in the form of a questionnaire. Questionnaires are easy to analyze. Data entry and tabulation can be easily done with many computer software packages. Questionnaires are familiar to most people.
  114.  Nearly everyone has had some experience completing questionnaires and they generally do not make people apprehensive. Questionnaires reduce bias. There is uniform question presentation. The researcher's own opinions will not influence the answer.  Mailed questionnaires are less intrusive.  When a respondent receives a questionnaire by mail, he/she is free to complete the questionnaire on his/her
  115. The main Components of a questionnaire  Identification data: respondent’s address, time and date of interview, code of interviewer, etc.  Instruction: Include clear and concise instructions on how to complete the questionnaire.  Information sought: the actual information needed - major portion of the questionnaire  Covering letter: brief purpose of the survey, who is doing it, time involved, etc.
  116. In designing the questionnaire, setting clear goals is essential:  Well-defined goals are the best ways to assure a good questionnaire design.  If the goals of a study can be expressed in a few clear and concise sentences, the design of the questionnaire becomes considerably easier.  Hence, ask only questions that directly address the study goals.  Avoid the temptation to ask questions because it would be "interesting to know".
  117.  You must do everything possible to maximize the response rate.  As a general rule long questionnaires get less response than short questionnaires.  Hence, keep your questionnaire short to maximize response rate.  Minimizing the number of questions is highly desirable, but we should never try to ask two questions in one.
  118.  In addition, the following issues need to be considered carefully:  Question content  Question wording  Response form  Question sequence
  119. 2. Question Wording: Using Shared Vocabulary 3. Response structure or format –  Open-ended Vs. closed questions a) Open Ended Questions  In open-ended questions respondents can give any answer.  They may express themselves extensively.  E.g. “fill in “ questions.
  120. Advantage  Permit an unlimited number of answers  Respondents can qualify and clarify responses  Permit creativity, self expression, etc. Limitations  responses may not be consistent.  Some responses may be irrelevant  Comparison and statistical analysis difficult.  Articulate and highly literature respondents have an advantage
  121. b) Closed Questions  Generally preferable in large surveys.  dichotomous or multiple-choice questions. Advantages  Easier and quicker for respondents to answer  Easier to compare the answers of different respondents  Easier to code and statistically analyze  Are less costly to administer  reduce the variability of responses  make fewer demands on interviewer skill, etc.  don’t discriminate against the less talkative
  122. Limitations  Can suggest ideas that the respondents would not otherwise have  too many choices can confuse respondents  During the construction of closed ended questions:  The response categories provided should be exhaustive.  They should include all the possible responses that might be expected.  The answer categories must be mutually exclusive.
  123. 4) Question Sequence – the order of the questions  The order in which questions are asked can affect the response as well as the overall data collection activity.  Grouping questions that are similar will make the questionnaire easier to complete, and the respondent will feel more comfortable.  Questions that use the same response formats, or those that cover a specific topic, should appear together.
  124. Questions that jump from one unrelated topic to another are not likely to produce high response rates.  So, transitions between questions should be smooth.  Each question should follow comfortably from the previous question.  Useful to present general questions before specific ones in order to avoid response contamination.  But at the same time, it is important to group items into coherent categories.
  125. 5) Physical Characteristics of a Questionnaire  An improperly laid out questionnaire can lead respondents to miss questions, can confuse them.  So, take time to design a good layout  ease to navigate within and between sections  ease to use the questionnaire in the field; e.g., questions on recto and codes on verso sides of the questionnaire  leave sufficient space for open-ended questions  questionnaire should be spread out properly.
  126.  If you put more than one question on a line some respondents might skip the second question.  Abbreviating questions will result in misinterpretation of the question. Formats for Responses  A variety of methods are available for presenting a series of response categories.  Boxes  Blank spaces
  127. Providing Instructions  Every questionnaire whether self administered or administered by an interviewer should contain clear instructions.  General instructions: basic instructions to be followed in completing it.  Introduction: If a questionnaire is arranged into subsections it is useful to introduce each section with a short statement concerning its content and purpose.
  128.  Specific Instructions: Some questions may require special instructions.  Interviewers instruction: It is important to provide clear complementary instruction where appropriate to the interviewer. 6) Reproducing the questionnaire  A neatly reproduced instrument will encourage a higher response rate, thereby providing better data.  Pilot Survey: The final test of a questionnaire is to try it on representatives of the target audience.  If there are problems with the questionnaire, they almost
  129. Data Processing and Analysis  Large volume of raw statistical information need to be reduced to more manageable dimensions if one is to see meaningful relationships in it.  i.e. data need to be analyzed.  Data analysis is the computation of certain indices or measures and searching for patterns of relationships.  It ranges from very simple summary statistics to extremely complex multivariate analyses.
  130. Data Preparation and Presentation  Data processing starts with the editing, coding, classifying and tabulation of the collected data. i) Editing: is the process of examining the collected raw data to detect errors and omissions.  Editing involves a careful scrutiny of the completed questionnaires to assure that the data are: Accurate Consistent with other facts gathered Uniformly entered
  131.  The editing can be done at two levels- on the field and in the office. a) Field level Editing  After an interview, field workers should review their reporting forms, complete what was abbreviated, translate personal short hands, rewrite illegible entries, and make callback if necessary. b) Central editing  The central editing takes place when all forms have been completed and returned to the office.
  132.  Data editors correct obvious errors such as entry in wrong place, recorded in wrong units, etc. ii) Coding  Coding refers to the process of assigning numerals to answers so that responses can be put into a limited number of categories or classes.  Data are transcribed from a questionnaire to a coding sheet.
  133.  The coding must be:  Appropriate, which implies that the classes or categories must provide the best partitioning of data for further analysis.  Exhaustive - there must be a class for every data item.  Mutually exclusive – category components should be mutually exclusive i.e. specific answers can be placed in one and only one cell in a given category set.
  134. iii) Classification and Tabulation  Classification is the process of arranging data in groups or classes on the basis of common characteristics.  Data having common characteristics are placed in similar classes.  Examples: males, females, second year students, etc.  Tabulation is the process of summarizing raw data and displaying it in compact form for further analysis.
  135.  Can be done by hand or by electronic devices such as the computer.  The choice is made largely on the basis of the size and type of study, alternative costs, time pressures and the availability of computer facilities.  In the case of computer tabulation computer programs such as SPSS, excel, STATA, etc. could be used.
  136.  Tabulation provides the following advantages: It conserves space and reduces explanatory and descriptive statement to a minimum. It facilitates the process of comparison It facilitates the summation of items and the detection of errors and omissions It provides a basis for various statistical computations such as measures of central tendencies, dispersions, etc.
  137. Data analysis Tools  By analysis we mean the computation of certain indices or measures along with searching for patterns of relationship that exist among the data groups. Analysis, particularly in case of survey or experimental data, involves estimating the values of unknown parameters of the population and testing of hypotheses for drawing inferences. Analysis may, therefore, be categorised as descriptive analysis and inferential analysis (statistical analysis) 137
  138. “Descriptive analysis is largely the study of distributions of one variable. This sort of analysis may be in respect of one variable (described as uni-dimensional analysis), or in respect of two variables (described as bi-variate analysis) or in respect of more than two variables (described as multivariate analysis). In this context we work out various measures that show the size and shape of a distribution(s) along with the study of measuring relationships between two or more variables. Descriptive statistics concern the development of certain indices from the raw data. 138
  139.  Inferential analysis is concerned with the various tests of significance for testing hypotheses in order to determine with what validity data can be said to indicate some conclusion(s). It is also concerned with the estimation of population values. It is mainly on the basis of inferential analysis that the task of interpretation (i.e., the task of drawing inferences and conclusions) is performed. 139
  140.  Inferential statistics are also known as sampling statistics and are mainly concerned with two major type of problems: (i) the estimation of population parameters, and (ii) the testing of statistical hypotheses.  The important statistical measures that are used to summarise the survey/research data are: (1) measures of central tendency or statistical averages; (2) measures of dispersion; (3) measures of asymmetry (skewness); (4) measures of relationship; and (5) other measures.
  141. Univariate analysis  The initial uni-variate analysis may be the preparation of descriptive statistics.  Descriptive statistics summarises main patterns  Tables – frequency tables  Graphs/diagrams – bar and pie charts, histograms  Statistics – mean, median, range, etc.  smallest value, largest value, and number of observations for every variable used
  142. Variable Mean Std. Dev. Min. Max. No. Obs. X1 9.2265 0.7659 7.4434 10.2058 309 X2 2.2510 3.8311 16.2883 11.2552 309 X3 0.6462 0.0824 0.4020 0.7944 309 X4 30.5487 19.6497 2.4200 83.5000 309 X5 47.1290 21.6031 5.5340 91.7428 309 X6 1.2508 1.0495 1.8074 3.5566 309 X7 61.9448 19.2422 10.9500 96.3800 309 Example:
  143. Univariate analysis  Measures of central tendency: a value typical for the data 1. Mean (Arithmetic, Geometric, Harmonic, Weighted) 2. Median - mid-point value; 3. Mode
  144. Univariate analysis Measures of dispersion 1. Range 2. Variance and standard deviation 3. Skewness & Kurtosis (measuring the degree of Asymmetry)
  145. Univariate analysis Have the same mean But different dispersions
  146. Bivariate analysis This is analysis of two variables to examine if they are correlated NB:- co-variation does not always imply causation  Scatter plot/diagram: values of the two variables plotted on each axis; strong relationships can be identified by scatter diagrams
  147. Scatter plot of a positive association Income and livestock ownership 0 10 20 30 40 50 60 0 200 400 600 800 1000 1200 Income Livestock
  148. Scatter plot of a negative association Income & illitracy rates (%) 0 20 40 60 80 100 0 200 400 600 800 1000 1200 Income Rateofilliteracry(%)
  149. Scatter plot of no association Income and household size 0 2 4 6 8 10 12 0 200 400 600 800 1000 1200 income hhsize
  150. Bivariate analysis Correlation analysis  Correlation analysis goes further by computing numerical values reflecting strength of relationship III III IV Mean y Mean x
  151. Bivariate analysis  If observations are in I & III, positive association (x-mean x)*(y- mean y) > 0 (both positive) Hence for positive association ∑(x-mean x)*(y-mean y) > 0  If observations are in II & IV, negative association (x-mean x)*(y-mean y) < 0 (when one is positive the other is negative) Hence for negative association ∑(x-mean x)*(y-mean y) < 0  To get a standard measure that ranges between -1 and 1 divide the above by an appropriate value
  152. Bivariate analysis        2 2 2 22 2 ( )( ) ( ) ( ) / / / x x y y r x x y y xy x y n x x n y y n                           
  153. Bivariate analysis  The above is Pearson’s correlation coefficient or Pearson’s r or simply correlation coefficient  Captures linear relationship between variables; non-linear relationship are not captured  Lies between -1 & 1:- r=0: no significant relationship  r=1: perfect positive relationship  r=-1: perfect negative relationship  Spearman’s rho/rank correlation coefficient (ρ) mainly for ordinal variables
  154.  Multivariate analysis explores the relationship between three or more variables  Some of the relationships identified in bi-variate analysis can be spurious - when there is no real relationship. Multivariate analysis Income Nutrition Livestock Spurious correlation?
  155. Multivariate analysis Spurious variables are there intervening or moderating variables?  e.g., is the effect of income on nutrition moderated by who controls the budget (males or females)?  Analysis should control for the effects of additional variables  Multiple regression analysis (econometrics) controls for all important variables on which data are available. Yi = β0 + β1*X1 + β2*X2 + … + βn*Xn + εi Yi = β0 + βi∑Xi+ εi
  156. Multivariate analysis where X and Y are the independent and dependent variables βi = coefficient parameters to be estimated; increase/decrease in Y when X changes by one unit (controlling for other factors) εi = random error term; difference between estimated values of Y and real values of Y; and assumptions on εi
  157. Multivariate analysis  How are the parameters (βi) estimated?  The widely used method is ordinary least squares  In least squares method the difference between the expected values of Y from the regression and the real values of Y is minimised = the error terms are minimised  For a regression with one variable: Yi – (β0 + β1*X1)= εi Overall test (F-test): the null hypothesis for the overall test is ‘all the coefficient of the regression are zero?’ (no explanatory power)
  158. Multivariate analysis Ho: β1 = β2= β3 = … = βn = 0  Test for a single variable (t-test): Does a particular independent variable adds significantly to the explanation? Ho: βi = 0. Several Econometric problems are expected. Inferential statistics: Is the pattern described in the sample likely to apply to the whole population? Interval estimates estimates the margin of error in sample statistics compared to population values It is the range around the sample statistics where the true population value is likely to be found
  159. Inferential statistics and hypothesis testing Sample statistics is here Population value is likely to be found within this interval
  160. Inferential statistics and hypothesis testing  Example: 95% confidence interval for mean  statistical theory tells us 95% of the distribution will be within +/– two standard errors of the mean  standard error of the mean Sm = s/√n where s = standard deviation; n = sample size  If mean from the sample is m, then the 95% confidence interval for the population mean m - 2Sm ≤ x ≤ m + 2Sm
  161.  Tests of statistical significance: testing whether possible values of the population are supported by sample statistics  Given a null hypothesis  Sample results can be different from the null hypothesis because of sampling error or  null hypothesis is wrong  More than 5% chance of getting the statistics due to random error conventionally is not accepted
  162. Chapter Three:- Closure of the Research Process  Types of reports  Components of the research report  Academic writing skills  Writing procedures and findings, writing conclusions. 162
  163. Writing the Research Report  Introduction  The intrinsic value of a study can also be easily destroyed by poor report preparation.  A well-presented study can impress the reader more than another study with greater scientific quality but a weaker presentation.  Hard work and excellence alone do not guarantee that research will have an impact.  So, good research alone is insufficient.
  164. Introduction  Hence, researchers must communicate clearly and fully their research results through a report.  A research report is a written means of communication the research result.  Writing is a process- it takes time, and effort and improves with practice.  When writing the research report it would be important to consider: What is the purpose of the report?  Who will read the report?  How will the report be used? etc.
  165. The Writing Process Generally the writing process has three major steps: i) Pre-writing: prepare to write by arranging notes on the literature, making lists of ideas, outlining, completing bibliographic citations, footnotes, and organizing comments on data analysis. ii) Composing: get your ideas onto paper as a first draft by free-writing –draft report.
  166. The Writing Process iii) Rewriting: evaluate and polish the report by improving coherence, proofreading for mechanical errors, checking citations, and reviewing voices and tenses.  This step actually involves two related procedures:  Revising is the process of inserting new ideas, adding supportive evidences, deleting or changing old ideas, etc.  Editing is the process of cleaning up -spelling, grammar usage, verb tense, sentence length and paragraph organization.
  167. Types Of Research Reports:- Short and Long Reports Short Reports: are more informal and are appropriate for studies in which the problem is well defined, of limited scope, and for which methodologies are simple and straightforward.  Example: interim reports. At the beginning, there should be a brief statement on the problem. Next comes the conclusions and recommendations, followed by findings that support the conclusions.
  168. Types of Research Reports Long Reports: Long reports are long and follow well- defined formats.  They are of two types, the technical or base report and the popular report.  Which of these to use depends chiefly on the audience and the researcher’s objectives. i) The technical report  This report should include full documentation and detail - it is the major source document.
  169. Types of Research Reports  It contains information on the:  sources of the data,  sampling design,  data gathering instruments,  data analysis methods, as well as  a full presentation and analysis of the data.  Conclusions and recommendation should be clearly related to specific findings.
  170. Types of Research Reports ii) The popular report  The popular report is designed for the non-technical audience with no research background and may be interested only in results rather than on methodology. Decision makers need help in making decisions – e.g. policy briefs.  Popular report should encourage rapid reading, quick comprehension of major findings and prompt understanding of the implication and conclusions.
  171. Types of Research Reports Report format for long reports  Two arrangements are typically used – the logical format and the psychological format. The logical format  the introductory information covering the purpose of the study, the methodology and limitations is followed by the findings.  The findings are analyzed and then followed by the conclusions and recommendations.
  172. Types of Research Reports The psychological format: The conclusions and recommendations are presented immediately after the introduction with the findings coming later.  Readers are quickly exposed to the most critical information i.e. conclusions and recommendations.  If they wish to go further they may read on into the findings, which support the conclusion. Other report formats -the chronological report, which is based on time sequence or occurrence.
  173. Components of a technical report  While some may be dropped, other may be added and their order may vary from one situation to another, a research report contains several components or elements.  In general, there are three parts: the prefatory pages, the body of the report and the appended sections. A) Prefatory pages – this section includes the title page, tables of contents, table of charts and illustrations, synopsis (summary, abstracts), acknowledgement (if any), Acronyms etc.
  174. Components of a technical report The Title page:- the title page should include four items: the title of the report, the date, for whom prepared and by whom the report was prepared.  A satisfactory title should be brief, but should at least include:  The variables included in the study, the type of relationship between the variables, and the population to which the results may be applied.
  175. Components of a technical report The table of contents: – any report of several sections should have a table of content. Abstract – this is a short summary.  It goes first in the report, but should be written last.  It helps the reader determine whether the full report contains important information.  It is essential that your abstract includes all the keywords of your research.
  176. Components of a technical report  An abstract should briefly:  Re-establish the topic of the research.  Give the research problem and/or main objective of the research.  Indicate the methodology used.  Present the main findings and conclusions  It must be short, because it should give only a summary of your research.
  177. Components of a technical report Common Problems in preparing the Abstract  Too long and too much detail: Abstracts that are too long often have unnecessary details.  The abstract is not the place for detailed explanations of methodology or the context of your research problem.  Too short: Shorter is not necessarily better.  You should review your abstract and see where you could usefully give more explanation.
  178. Components of a technical report B) The body of the report – contains the introduction, findings, summary and conclusions and recommendations. 1) Introduction – will mostly contain the same material as the introduction to your proposal  It introduces the research by giving the background, presenting the research problem, indicating the objectives, the rationale or significance and the scope and limitations.
  179. Components of a technical report Introducing the rest of the report The last paragraph of the introduction should explain the organization of the rest of the report Example: “Section two reviews the relevant literature. In Section 3, we describe the data we have collected. In Section four, we test our hypothesis using this data. Section five concludes and makes recommendations for future research.”
  180. Components of a technical report Common problems in writing the introduction 1. Too much detail, and hence too long: detailed descriptions of method, study site and results should come in later sections. 2. Repetition of words, phrases or ideas. A high level of repetition makes your work look careless. 3. Unclear problem definition: Without a clear definition of your research problem, your reader is left with no clear idea of what you were studying. 4.Poor organization
  181. Literature Review The report also includes a literature review. Literature means the works you consulted in order to understand and investigate your research problem. It should justify the following ideas: Other people are interested in the general topic Other studies left the problem unsolved which leaves a gap in the literature Your study fills the gap at least a little bit
  182. Literature Review Common Problems: 1. Trying to read everything: if you try to be comprehensive you will never be able to finish the reading!  The literature review should not provide a summary of all the published work that relates to your research, but a survey of the most relevant and significant work.
  183. Literature Review 1. Reading but not writing: Writing takes much more effort than reading- don't put writing off until you've "finished" reading. 2. Not keeping bibliographic information: When preparing your reference you might notice that you have forgotten to keep the information you need.  To avoid this nightmare always put references into your writing. Answers at least two main questions:  How was the data collected or generated?
  184. The methods  How was it analyzed?  The data collection step covers at least four items:  the target population that is being studied and the sampling methods used.  the research design used and the rationale for using it including the sample size,  the materials and instruments used often with a copy of these materials in the appendix,  the specific data collection method (survey, observation or experiment)
  185. The methods  Your methodology should make clear the reasons why you chose a particular method or procedure. Common Problems 1. unnecessary explanation of basic procedures 2.problem blindness: Do not ignore significant problems or pretend they did not occur.  Often, recording how you overcome obstacles can form an interesting part of the methodology.
  186. Findings and Discussions It is an organized presentation of results and is generally the longest section of the report.  The Results Section includes:  statement of results: the results are presented in a format that is accessible to the reader (e.g. in graphs, tables, diagrams or written text).  explanatory text: all graphs, tables, diagrams and figures should be accompanied by text that guides the reader's attention to significant results.
  187. Findings and Discussions The Discussion Section:  The discussion section provides explanation of the results and includes:  Explanation of results: comments on whether or not the results were expected and presents explanations for the results, particularly for those that are unexpected or unsatisfactory.  References to previous research: comparison of the results with those reported in the literature, or use of the literature to support a claim or a hypothesis.  Deduction: a claim for how the results can be applied more generally.
  188. Summary and Conclusion  The summary section presents: • What was learned • The shortcomings of what was done • The benefits, advantages, applications, etc. of the research (evaluation).  The conclusions should follow logically from the discussion of the findings.
  189. Summary and Conclusion Common Problems 1. Too long. The conclusion section should be short not more than 2.5% of an entire piece. 2. Too much detail. Conclusions that are too long often have unnecessary detail. 3. Failure to reveal difficulties encountered: Negative aspects of your research should not be ignored.  Problems, drawbacks can be included in your conclusion section as a way of qualifying your conclusions (i.e. pointing out the negative aspects.
  190. Recommendations – It involves suggested future actions.  It makes easy reading if the recommendations are again placed in roughly the same sequence as the conclusions.  The recommendations could be for further study, to test, deepen or broaden understanding in the subject area or for managerial actions.  should take into consideration the local conditions, constraints, feasibility and usefulness of the proposed solutions.
  191. The appended section – this includes appendix and bibliography.  Appendix – complex tables, statistical tests, supporting documents, copies of forms used, detailed description of the methodology, instructions to field workers, and any other evidence that may be important.  The annexes should contain any additional information needed to enable professionals to follow your research procedures and data analysis.
  192. The appended section  Examples of information that can be presented in annexes are:  tables referred to in the text but not included in order to keep the report short;  lists of study sites, -districts, villages, etc. that participated in the study;  questionnaires or check lists used for data collection.
  193. Presentation Consideration  Reports should be physically inviting, easy to read and match the comprehension abilities of the designated audiences (reader).  Style of writing: Remember that your reader:  Is short of time  Has many other urgent matters demanding his or her interest and attention  Is probably not knowledgeable concerning ‘research jargon’  It is always good to use words that convey thoughts accurately, clearly and efficiently.
  194. Presentation Consideration  Therefore the rules are:  Simplify- Keep to the essentials.  Justify- Make no statement that is not based on facts and data.  Quantify when you have the data to do so - Avoid ‘large’, ‘small’; instead, say ‘50%’, ‘one in three’.  Use short sentences.  Be consistent in the use of tenses (past or present tense).
  195. Presentation Consideration Layout of the report: A good physical layout is important since it will:  make a good initial impression,  encourage the readers, and  give them an idea of how the material has been organized so the reader can make a quick determination of what he will read first.  Poor reproduction, dirty typewriter type, incorrect spelling and typographic errors, overcrowding of text, inadequate labeling of charts and tables, etc. reduce the credibility of a report.
  196. Presentation Consideration  So, make sure that there is:  An attractive layout for the title page and a clear table of contents.  Consistency in margins, spacing, headings and subheadings,  Numbering of figures and tables, provision of clear titles for tables, and clear headings for columns and rows, etc.  Accuracy and consistency in quotations and references.
  197. Presentation Consideration  Revising and finalizing the text: the following questions should be kept in mind when revising:  Have all important findings been included?  Do the conclusions follow logically from the findings? If some of the findings contradict each other, has this been discussed and explained? Have weaknesses in the methodology, if any, been revealed?  Are there any overlaps in the draft that have to be removed? And is it possible to condense the content?
  198. Presentation Consideration  Do data in the text agree with data in the tables? Are all tables consistent (with the same number of informants per variable), are they numbered in sequence, and do they have clear titles and headings?  Is the sequence of paragraphs and subsections logical and coherent? Is there a smooth connection between successive paragraphs and sections? Is the phrasing of findings and conclusions precise and clear?  Perform a spell check and grammar check.
  199. Briefings (presentation)  Good presentation improves both the research and the reputation of the researcher.  A successful briefing typically requires a condensation of a lengthy and complex body of information.  About 20 minutes presentation is usually required.  An outline of what one is going to say includes  Opening  Findings and conclusions  Recommendations
  200. Briefings (presentation)  The most important thing to keep in mind:  The time will usually pass a lot more quickly than you think  Keep focused on the main ideas: The motivation, the problem, and the main results  You do not have to mention all of the difficulties and shortcomings; people can ask during the presentation  You do not need to mention response rates or sample size misspecifications unless these are very important; people can ask
  201. Briefings (presentation)  Organizing slides:  A slide should contain a handful (25) of key points; it should not fill the page  Slides should not contain your entire presentation, just the key things to remember  Graphics can be useful if they tell the story
  202. Summary 202
  203.  Please develop scientific literacy  Scientific literacy is the capacity to understand scientific knowledge; apply scientific concepts, principles, and theories; use scientific processes to solve problems and make decisions; and interact in a way that reflects core scientific values. Essay writing:-  Introduction  Body  conclusion (summary) 203
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