Methods and Challenges in Data Collection

1. FOREWARD Authors as Adams, Khan, Hafiz and Raeside (1), suggest nearly regularity for selective information army of battle, basing on the situation, warning from attainable threats to the validity and reliability of entropy self-collected. Whatever the method of selective information collection chosen ( annotations, experimentation, survey, interviews, diary method, amaze study, information storage, triangulation), there argon several theory that need to be guideed since the beginning (1) the ch e really(prenominal)enges born from the reputation of the explore and level of detail the police detective motivation to reach, then by time and bud spend a penny available, so careful consideration and course of studyning of info collection is required.There are some common principles, for examples try to hap as practically as possible merciful misplays, analyze all single-valued functionful entropy rather of the only one which seems to fit in the theory, running play multiple tests to check eventual defects. Collecting data is crucial in many unalike cranial orbit of business interest, e. g. from concurrency paygrade to create a feigning for the estimation of pipe price, before to meet the provider for the final negotiation.For example, first strategy adopted from exhort and proposal department, for the evaluation of piping price impact, is to approximate raw material steel price and add a certain share which consider amount constitute of ownership. Second strategy foundation consider different elements which compose final price, starting from inauguration of data instead of estimate a percentage only. This is one of the key elements Bebell, ODwyer, Russel and Hoffmann (2) studied the splendor of engine room in the last past eld to help researcher to evaluate and confute data availability and validity, for example triangulating the same data.In any case, three-figure methods doesnt contextualizes in the situation, consider ing for example the market situation, the human race ability to concretize business relationship, 2. CHALLENGES 3. 1 informant of data World is full of data and opinion, the advent of engineering science and internet al wiped out(p) to many single-valued functionrs all over the world to fasten access to the weave for those who take a shit access, initiation of millions of articles, opinion, paper, studies, According to Bebell, ODwyer, Russel and Hoffmann (2) the use of laptop and nternet by learners and scholars, in both cases mattered that nigh 50% or more use technology to make first research and to deliver instruction. The rudimentary IT organization in a statistical authority has a very important role in Web-based data collection, since data collection system has cardinal very broad component an electronic questionnaire, and everything else associated with abject that electronic questionnaire to and from a respondent, including systems and security consideratio ns (3).Since the go around result is get if the questionnaire, interview, survey, is focused as much as possible to the argument of research and to participant that salubrious get the argument, source(s) of data, have to be identified since the beginning, peradventure during the data collection planning stage. Doing this, the researcher optimizes his / her time, neutraliseing to source data time per time is need. Researcher has to repeal interpretation and misunderstanding in the question, in order to get invalid reactions.This imply that for example, the questionnaires received, if duly filled, whitethorn not be very useful because take overt meet the requirements, some otherwise, target of the research digestnot be reached. Infact rate of response john results too low so unacceptable, and potentially lot can look to not respond since they dont know about the question. Initial investment of the time to plan the job, keep down creating questionnaires inefficient to th e researcher. When we face to questionnaires which dont know whats talking about, the first chemical reaction is to leave it blanks or give confused answers.For these reasons, hit-or-miss sampling techniques, stratified random sampling techniques consolidation with pre-test, are crucial in order to deflect eventual fairness, big enemy of the study, even if the researcher has to consider that a pre-test may sensitize or polarize the persons behavior and consequently, cleanse performance on the post-test. Some methods for avoiding this issues, will be analyzed in the next chapter strategies 3. 2 Characteristics of collected data The target of the researcher is to get the data as objective as possible and the best response rate, not only in name of numbers unless as much example as possible (2).It authority that collects objective data, makes it stronger and solid the research, and open to any new research or alternative etymons. Some examples of objective criteria could be * foodstuff value * Scientific riseings * Efficiency of the model * nonrecreational standards defined * Equal treatment * Tradition * court-ordered (court) * Reasonableness Collecting the right data, allows the researcher to get representative answers which help to find a solution to the problem that he / she places, otherwise the study can be compromise since the beginning, or can drive the researcher to solution not representative of reality.For example, participation can decide to capture data of sparing from a certain database characterized by having certain trueness, i. e. two decimal places at the end of abstract, the researcher have to know that the result is affected by a certain error value. Infact, even if minimal error is occasionally acceptable, in some cases can charter to unacceptable inaccuracy or even to the failure of the project. For this, determine the level of tolerated error is need during the collection of quantitative data. Techniques and devices for th e quantitative collection have to be characterized by a certain tolerable range of error. 3. 3 entropy collectionTwo main different categories can be considered primary (data not available by prior research, ) and secondary (data are available elsewhere). In both cases, when were accumulation quantitative data, it is often tempting to record and use only which results that correspond to priori test, experiments or theory, especially when the expected results are so different from the ones got. However, could go past that especially these unexpected data shown problems with the data-based procedures, so these values should not be ignored. culture but not least, assertiveness of the researcher avoid to influence the questionnaire or data search.For example, supplier A has quoted 100 and supplier B = 70, C = 72, D = 68 for the same identical portion. Technical evaluation has been done for all it means that, the same package has more or less 40% of passing in price canvasd than A. It may seems an anomaly, in well-nigh of the cases that is since one supplier is trying to getting much money, but a careful analysis can lead to evaluate that B and C quoted very low at the beginning, in order to get the PO, foreseeing to recover later on adding some parts, reaching or going over price of A. 3. 4 Cost and timeData collection process can requires observation of the research phenomenon, over than time for collection, surveys, This particularly happen in the retentiveitudinal studies, where data have to be analyzed at different time. Nevertheless, changes can authorize in the subjects during the observation period, so they can be influenced. Cost can limit the data accomplishment phase, limiting the collection and right type of data need to conduct the research. As the size increases, variant decreases. Moreover instrumentation with right accuracy, basing on the accuracy target level of the research, can be a limit for the research. . STRATEGIES TO OVERCOME 4. 5 Maintain received data Reliability and validity can be proved, without manipulation, and maintain the opportunity eventually to examine again, reinforcing the conclusions. It means that, since the best and quick results are gain finished computer, memory disk should be prerequisite to stick in the data. Other reason is that longer is a study, prouder(prenominal) is the possibility that historical data are necessary since the time tends to change the conditions. Moreover, pre-test need, when done, need to be stored. 4. 6 Pre-testThey can influence the subjects, so post-test different from pre-test can avoid this effect. Multiple independent trials minimize error when collecting quantitative data, asking to distinct meeting to run the test or experiments aimed at collecting specific quantitative data. These 2 groups can compare the results, which should be the same. 4. 7 Clear and easy data blank document In order to avoid low rate of response, it has to be easy to use and clear, in English lyric or the language of the subjects, allowing the participants to give informative and accurate.Over this, the blank is to be candid and quickly to be filled, otherwise participants can be discouraged. 4. 8 Double check source and people for data collection When data collection is delegated to other people or relies to the use of internet, the collection is by other people. For example, company which get information by dint of surveys under payment, its a very high quality and quantity way to complete surveys, but need to be analyzed whose responder are really working on the answer or are interested to get the reward only.Temptation to fake data to enhance results is common when happens, the validity of the research becomes doubt. For sure most of the times mistakes are unwanted, and the response need to be identified. One way to cipher this problem should be solved using technology (2). For instance, software can help to create an mediocre, associate and evaluate which are completely out of average and why, since they could be representative of the survey or referable to the low knowledge of the responders, collect all the evaluable data finding eventual correlation between the variables.In conclusion, find the middle way in optimizing the additional cost and reduction of time thanks to technology, is a cover challenge for the researcher which would share his / her research to others, since research designed to solve problems in medium long terms, rather than short terms, is increasingly required in todays business environment. REFERENCES 1) Adams, John Khan, Hafiz T A Raeside, Robert (2007) Research Methods for Graduate Business and well-disposed Science Students.Sage India 2) Damian Bebell, Laura M. ODwyer, Michael Russell, Tom Hoffmann 2010 Concerns, Considerations, and New Ideas for Data Collection and Research in Educational engine room Studies 3) Richard W. Swartz and Charles Hancock 2002 Data collection through web-bas ed engineering 4) Reetta Raitoharju1, Eeva Heiro2, Ranjan Kini3, and Martin DCruz 2009 Challenges of multicultural data collection and analysis experiences from the health information system research