Execution of Project Through Generalization and Interpretation Essay Example

Execution of Project Through Generalization and Interpretation Essay Example Execution of Project Through Generalization and Interpretation Essay Execution of Project Through Generalization and Interpretation Essay Execution of the project (implementation phase of the project) proceeds on correct lines, the data to be collected would be adequate and dependable. The researcher should see that the project is executed in a systematic manner and in time. If the survey is to be conducted by structured questionnaires, data can be readily machine-processed. In such situation, questions as well as the possible answers may be coded. If the data are to be collected through interviewers, arrangements should be made for proper selection and training of the interviewers. Steps should be taken to ensure that the survey is under statistical control, so that the collected information is in accordance with the pre-defined standard of accuracy. Generalizability: It is the responsibility of the researcher to provide evidence regarding the reliability, validity and generalizability of the findings. The report should clearly identify the target population to which the findings apply. Factors that limit the generalizability of the findings, such as the nature and representativeness of the sample, mode and time of data collection, and various sources of error should be clearly identified. The reader should not attempt to generalize the findings of the report without explicit consideration of these factors. Interpretation and Conclusions: The findings should be reported in an objective and candid way. The interpretation of the basic results should be differentiated from the results parse. Any assumptions made in interpreting the results should be clearly identified. The limitations of the research should be discussed. Any conclusions or recommendations made without a specification of the underlying assumptions or limitations should be treated cautiously by the reader. WHAT IS GENERALIZATION? Generalization is to which extent the research and the conclusions of the research apply to the real world. It is not always so that good research will reflect the real world, since we can only measure a small portion of the population at a time. In fact every research study, somehow tries to relate observations to theory. If a hypothesis is tested repeatedly then researcher can move to generalization and construct a theory out of it. This is the real objective of the research. [pic] Generalization identifies commonalities among a set of entities. The commonality may be of attributes, behavior, or both. For example, a statement such as All windows have a title expresses a common attribute among all entities that are considered windows. Similarly, the statement, All windows can be resized. expresses a common behavior that all windows provide. Generalizations are usually easy to recognize as they contain words like all and every. Generalization is an essential component of the wider scientific process. In an ideal world, to test a hypothesis, you would sample an entire population. By Martyn Shuttleworth (2008) You would use every possible variation of an independent variable. In the vast majority of cases, this is not feasible, so a representative group is chosen to reflect the whole population. For any experiment, you may be criticized for your generalizations about sample, time and size. You must ensure that the sample group is as truly representative of the whole population as possible. For many experiments, time is critical as the behaviors can change yearly, monthly or even by the hour. The size of the group must allow the statistics to be safely extrapolated to an entire population. In reality, it is not possible to sample the whole population, due to budget, time and feasibility. For example, you may want to test a hypothesis about the effect of an educational program on schoolchildren in the US. For the perfect experiment, you would test every single child using the program, against a control group. If this number runs into the millions, this may not be possible without a huge number of researchers and a bottomless pit of money. Thus, you need to generalize and try to select a sample group that is representative of the whole population. A high budget research project might take a smaller sample from every school in the country; a lower budget operation may have to concentrate upon one city or even a single school. The key to generalization is to understand how much your results can be applied backwards to represent the group of children, as a whole. The first example, using every school, would be a strong representation, because the range and number of samples is high. Testing one school makes generalization difficult and affects the external validity. You might find that the individual school tested generates better results for children using that particular educational program. However, a school in the next town might contain children who do not like the system. The students may be from a completely different socioeconomic background or culture. Critics of your results will pounce upon such discrepancies and question your entire experimental design. Most statistical tests contain an inbuilt mechanism to take into account sample sizes with larger groups and numbers, leading to results that are more significant. The problem is that they cannot distinguish the validity of the results, and determine whether your generalization systems are correct. This is something that must be taken into account when generating a hypothesis and designing the experiment. The other option, if the sample groups are small, is to use proximal similarity and restrict your generalization. This is where you accept that a limited sample group cannot represent all of the population. If you sampled children from one town, it is dangerous to assume that it represents all children. It is, however, reasonable to assume that the results should apply to a similar sized town with a similar socioeconomic class. This is not perfect, but certainly contains more external validity and would be an acceptable generalization. Forms of Generalization: One of the four forms of generalization is hierarchy. In the case of hierarchy, the commonalities are organized into a tree structured form. At the root of any sub tree are found all the attributes and behavior common to all of the descendents of that root. This particular kind of tree structure is referred to as a generalization/specialization hierarchy because the root provides more general properties shared by all its descendents while the descendents typically add specializing properties which make them distinct among their siblings and their siblings descendents. The second form of generalization is genericity. In the case of genericity, the commonality is expressed with the aid of a parameter. Various specializations are distinguished by what they provide for the parameter. For example, using genericity it is possible to represent the common properties of a stack through the generalization of a stack of anything, where anything represents the parameter. Specialized forms of this generalization are stack of integers and stack of characters. The third form of generalization is polymorphism. Polymorphism captures commonality in algorithms. An algorithm may have a nested if-then-else (or case statement) logic which tests for the exact type of an object which it is manipulating. The algorithm performs some operations on the object based on the exact type of the object. However, in many algorithms the operations to be performed are the same, only the type of the object on which they are performed varies. Thus, the algorithm need not know the exact type of the object. The algorithm only needs to know that the object can respond to the invocation in some manner. The fourth form of generalization is patterns. A pattern expresses a general solution (the key components and relationships) to a commonly occurring design problem. The attributes and behavior of the individual components are only partially defined to allow the pattern to be interpreted and applied to a wide range of situations. For example, a wheeled vehicle pattern might be defined in terms of the components wheel, axle, frame, body and power source. The pattern would also show how these components would be arranged in relation to each other (e. g. , the axle must connect two wheels). Example of the wheeled vehicle pattern are automobile, horses of Generalization: |All apples are red. | |All buildings are square. | |Anyone could tell you the laws of physics. | |Everyone is literate these days. | Image Example of Generalization: | | [pic] An n-cube can be projected inside a regular 2n-gonal polygon by a skew orthogonal projection, shown here from the 2-cube to the 10-cube. Generalization Assess Knowledge Assess knowledge is used to not only judge the generalization rules, but also assess the generalization quality, such as square root law. For example, when we use deduced knowledge to reason in map generalization, it will trigger process knowledge. Also the application of process knowledge needs the parameters about descriptive knowledge. Knowledge representation is an important section of information system design and operation. It symbolizes and formalizes the knowledge of experts field and translates them into the form which computer can recognize and process. But with the limitation of computer language, we often use the formal knowledge representation to realize the automatic process. Most knowledge representations are based on logic, relationship, object, regulation, semantic network, model and ontology (Kong, 2001). For cartographic generalization system, the difficulty in knowledge representation is how to express the knowledge of cartographic experts with systematic and integrated method, and eventually to solve real problems. Interpretation: Interpretation is the process by which meaning is attached to data. Interpretation is a creative enterprise that depends on the insight and imagination of the researcher, regardless of whether he/she is a qualitative analyst working closely with rich in-depth interview transcripts or ‘thick description’ based upon intense observation or, at the other extreme, a quantitative researcher carrying out a complex multivariate statistical analysis of a massive dataset. In both instances, interpretation, the way in which the researcher attaches meaning to the data, is not mechanical but requires skill, imagination and creativity. Purpose: Interpretation is used for drawing inferences from data, expounding/exposing relationships and process underlying the findings, searching for broader meaning of research findings, understanding and explaining what has been observed in the study. It is also used for providing theoretical conceptions to serve as a guide for further research. Interpretation opens avenues of intellectual adventure and simulates the quest for knowledge. ’Post-factum’ interpretation translates findings of exploratory research into experimental research. Data Interpretation: Data interpretation is an essential element of mature software project management and empirical software engineering. As far as project management is concerned, data interpretation can support the assessment of the current project status and the achievement of project goals and requirements. As far as empirical studies are concerned, data interpretation can help to draw conclusions from collected data, support decision making, and contribute to better process, product, and quality models. With the increasing availability and usage of data from projects and empirical studies, effective data interpretation is gaining more importance. Interpretation of data for project control, here, the focus is on project execution. Factors such as the increasing distribution of development activities, the need for monitoring risks, or regulatory constraints have accelerated the introduction of data-based project management techniques into practice. However making valuable use of collected data is challenging and requires effective mechanisms for data interpretation Need For Interpretation: For better appreciation of findings and make others to understand the real significance of findings. To understand the abstract principles that work beneath findings, to link findings and results with that of others, arriving at generalization after repeated testing of hypothesis, to take decisions based on implications of results and to maintain continuity in research i. e. is to help further studies. Precautionary Tips for Interpretation: Interpretation is an art and requires great skill. Optimum use of data and techniques. No over or under or misinterpretation. No out of context interpretation. Look for generalization but no false or even broad generalization. No hurry, have patience. Be impartial, have correct perspective. Wrong interpretation would lead to inaccurate conclusions. Make correct use of statistical measures. Interpretation and analysis are highly interdependent. Evaluating Interpretation: Why evaluate? Evaluating your interpretation will tell you whether it’s working or not. To evaluate you must have clear objectives for your interpretation. There a re four kinds of interpretive objective: Learning objectives – what do you want visitors to know about the site? Emotional objectives – what do you want visitors to feel about the site? Behavioral objectives – what do you want visitors to do as a result of the interpretation? Promotional objectives – how do you want to present your organization? When do I evaluate? Evaluation is classified according to when it’s done in the interpretive process. Front-end evaluation is done while you’re developing your interpretive objectives. It answers questions such as ‘what do the audience already know about this topic? ’ and ‘what are they most interested in? ’ This way you can tailor your interpretation to your visitors’ knowledge and interests. Formative evaluation tests visitors’ reactions to trial versions of your interpretation. For example, proofs of leaflets and panels can be tested to see if they attract attention and communicate the right messages. This allows you to change the design or content to make sure it works. Remedial evaluation checks that once all the elements in a display are brought together they work – for instance, the lighting is appropriate, visitor flow patterns are optimized, and distraction/competition between elements is minimized. Summative evaluation answers the question ‘is our nterpretation meeting its objectives? ’ Summative evaluation is carried out once a project is implemented. How do I evaluate? A range of evaluation methodologies are available. They can be subdivided into: Quantitative methods which count and measure things. Here your data is already in the form of numbers or can be converted into numbers that can be analyzed statistically. Qualitative metho ds which attempt to describe your visitor’s opinions, attitudes, perceptions and feelings. This information will require further interpretation and organization. Phase |Method | |Front end |Focus groups | | |Questionnaire interview | |Formative |Observation | | |Simple interviewer administered | | |questionnaire | |Remedial |Observation | | |Simple interviewer administered | | |questionnaire | |Summative |All methods but primarily observation| | |and questionnaire | | |Critical appraisal | Examples for Interpretation: 1. Representativeness of the data – Comparison of distribution of characteristics among population, sample and response population 2. Take note of nature of questions and types of responses – Dichotomous question with either or type answer. – Multiple-choice questions which require only one answer. – Multiple responses to multiple choice questions. – Open-end questions. Frequency Distribution Of Age (Comparison of characteristi cs distribution among population, sample and response sample) Population Response Population Age in Years No. % No. % Up to 24 95 11. 6 69 13. 2 25-29 268 32. 6 173 33. 0 30-34 255 31. 0 165 31. 5 35-39 151 18. 4 85 16. 2 40-44 39 4. 7 27 5. 1 45-49 6 0. 7 1 0. 50-above 8 1. 0 4 0. 8 Total 822 100. 0 524 100. 0 3. Handling and interpreting unanswered – Not answered. – Can’t answer. – Don’t know. – Don’t want to answer. – Distributing proportionately among other categories. – Keeping as a separate category. – Estimating answer from other data contained in questionnaire. 4. Representing the data – Percentages (ratios proportions) should be computed in the direction of causal factor, if any. – Percentages should run only in the direction in which a sample is representative. Do not average percentages(without weighing by the size of samples) – Do not use very large percentage(e. g. 1200% increase) – Do not use too small a base(e. g. 33 1/3% for 1 in 3 ) Interpretation of correlation coefficients(r,rs) 0. 9 Very highdependable Chi-square test No (expected) frequency cell should have value less than 5(use Yates correction formula) Hypothesis testing Accepting null hypothesis on the basis of sample information does not mean or constitute the proof that hypothesis is true. It only that there is no statistical evidence to reject it Logically ordering the data so that questions can be raised and answered Cross tabulation of 2 or more attributes or variables is merely a formal and economical method of arranging the data so that the logical method of proof may be applied . Cross tabulation is an approximation of the controlled experiment, i. e; just thinking in terms of cause and effect. Cross tabulation may lead to spurious explanation. Examples: Family size v/s income (valid) No. of automobiles owned v/s brand of toothpaste preferred (spurious) Elaboration Is a process which is limited only by the analysis (his/her ability, patience and purposes) and by the nature of data? Check need for elaboration and what test variable to apply. Range of cross tabulations suggests test variables.