Methods and Challenges in Data Collection

1. FOREWARD Authors as Adams, Khan, Hafiz and Raeside (1), suggest some mode for info gathering, basing on the situation, warning from possible threats to the validity and reliability of entropy collected. Whatever the method of selective information collection chosen ( musings, experimentation, survey, interviews, diary method, case study, entropy storage, triangulation), there are several hypothesis that need to be considered since the beginning (1) the challenges born from the disposition of the research and level of detail the researcher want to reach, then by time and bud sound available, so careful consideration and planning of data collection is required.There are some common principles, for examples try to eliminate as a lot as possible human phantasms, analyze all useful data alternatively of the only one which seems to fit in the theory, run multiple tests to check eventual errors. Collecting data is crucial in many different scope of business interest, e. g. from concurrency evaluation to create a model for the estimation of pipe price, before to meet the supplier for the final negotiation.For example, first strategy adopted from command and proposal department, for the evaluation of piping price impact, is to evaluate raw material steel price and add a certain contribution which consider summate cost of ownership. Second strategy stooge consider different elements which compose final price, starting from source of data instead of estimate a percentage only. This is one of the key elements Bebell, ODwyer, Russel and Hoffmann (2) studied the importance of technology in the last past years to help researcher to evaluate and confute data availability and validity, for example triangulating the same data.In any case, valued methods doesnt contextualizes in the situation, considering for example the market situation, the human ability to concretize business relationship, 2. CHALLENGES 3. 1 fount of data World is full of data and opinion, the advent of technology and internet allow to many users all over the world to take on access to the clear for those who have access, source 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 resolvinged that about 50% or more use technology to make first research and to deliver instruction. The central IT organization in a statistical agency has a very important role in Web-based data collection, since data collection system has both very broad component an electronic questionnaire, and everything else associated with moving that electronic questionnaire to and from a respondent, including systems and security considerations (3).Since the best result is get if the questionnaire, interview, survey, is focused as much as possible to the argument of research and to participant that well see the argument, source(s) of data, have to be identified since the b eginning, perchance during the data collection planning stage. Doing this, the researcher optimizes his / her time, stay offing to source data time per time is need. Researcher has to deflect interpretation and misunderstanding in the question, in narrate to get invalid receipts.This imply that for example, the questionnaires received, if duly filled, may not be very useful because dont meet the requirements, former(a)wise, target of the research cannot be reached. Infact rate of response can results too low so unacceptable, and potentially people can decide to not respond since they dont know about the question. Initial investment of the time to plan the job, avoid creating questionnaires inefficient to the 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, random sampling techniques, stratified random sampling techniques integrating with pre-test, are cruc ial in order to avoid 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 terms of numbers but as much typical as possible (2).It convey that collects objective data, makes it stronger and unassailable the research, and open to any new research or alternative solutions. Some examples of objective criteria could be * grocery store value * Scientific findings * Efficiency of the model * Professional standards defined * Equal treatment * Tradition * Legal (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 ingest the researcher to solution not representative of reality.For example, company can decide to capture data of saving from a certain database characterized by having certain accuracy, 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 lead 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 the 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 previous research, ) and secondary (data are available elsewhere). In both cases, when were collect quantitative dat a, it is often allure 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 happen that especially these unexpected data shown problems with the data-based procedures, so these values should not be ignored. Last 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 piece of ground. Technical evaluation has been done for all it means that, the same package has more or less 40% of difference in price compared than A. It may seems an anomaly, in most 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 longitudinal studies, where data have to be analyzed at different time. Nevertheless, changes can pass away in the subjects during the observation period, so they can be influenced. Cost can limit the data acquisition phase, limiting the collection and right type of data need to conduct the research. As the size increases, variability 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 original 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 through computer, memory disk should be necessary to store the data. Other reason is that longer is a study, extravagantly 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 group 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 language or the language of the subjects, allowing the participants to give informative and accurate.Over this, the blank is to be aboveboard 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 reli es to the use of internet, the collection is by other people. For example, company which get information through 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 manipulate 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 solve this problem should be solved using technology (2). For instance, software can help to create an average, break and evaluate which are completely out of average and why, since they could be representative of the survey or due 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 concrete 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 Social 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 Technology Studies 3) Richard W. Swartz and Charles Hancock 2002 Data collection through web-based Technology 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