The First step in researching any research involves early preparation. This involves creating and recording a guideline that one will use as a guideline as one does the research. The tools required for research and the appropriate storage places are the things that need to appear in the guideline. The guidelines change with time and new things are added or removed depending on the demands of the research. The next step is to develop a reliable storage system. This is the place where the information from the research is then recorded,( Marzano, & Pickering, 2001). The recording system must have the date when the research is complete, and the time has been done if possible. This will make the retrieval of such files easy because the dates will make it easy to remember the research. It is also beneficial to record the place where the research took place (Marzano & Pickering, 2001).
The headings or the topics discussed in the research are extremely valuable and should reflect in the recording. Dividing the research done into sub-topics and subheadings is also useful when retrieving a file. It organizes the work into portions that contain related information. The researcher in this case would find it easy to retrieve the files of the interviewees if he had divided the files into subheadings and stored related files together (Oman & Pfleeger, 1997).
After dividing the work from research into subtopics and subheadings then the next thing is to assign a number, a letter or a word to the related files,( Bronfenbrenner, 1998). Finding the file will be easy for anyone looking for it. This is because the file number or letter has related information sorting research into piles, forming sub-topics. Assign codes and indices to the files in each pile for easy retrieval. This makes it easy to retrieve a file because each code represents a certain pile and the pile has related files (Mertins & Schallock, 1999).
Comparing and contrasting software tools
Software used by many people for qualitative data analysis has gained ground in different fields. This increased usage of software applications for data analysis arises from the fact that these software programs have the ability to manage large volumes of data. They are fast accurate and time-saving making them the best option for most disciplines. However, most of the resources provided by the software applications are underused due to a lack of the necessary skills required for their maximum potential. There are many software application programs available that can be used in qualitative data analysis. Some of these programs include NVivo, CAQDAS, ATLAS, MAXqda and hyper research (Bryman & Hardy, 2009).
Although NVivo is the most commonly used software data analysis software, there are others like the ALTAS and CAQDAS that perform the same tasks. However, the two have minor differences and similarities in their functioning.
ATLAS computer software is designed to support the interpretation of texts, manage the data, and has an in-built ability to draw meaningful information from the text. It supports large volumes of data, whether graphics, audio data, visual or textual information. The use of software depends on factors such as the ability of the software to import data from other soft wares. It also depends on its ability to code the information or data that needs to be analyzed. ATLAS software does not have the ability to house large and incorporate large amounts of data as compared to NVivo, (Oman, & Fleeter, 1997). This is because NVivo software has features that make it possible for the user to look for data from an external source according to the form in which one requires. One can import data in tabular form from an external source and NVivo will integrate the data. NVivo also provides the user with the pleasure of exporting any coded data or text. This can then be used to match each document the user requires. This is also useful as it helps the user to get the coding of the information correct because NVivo does further analysis on the case provided (Bronfenbrenner,1998).
Both ATLAS and NVivo provide useful online guidelines for the users. This is a common feature in both soft wares, providing users with a smooth time as they get acquitted with the software that they are using. However, ATLAS software is easier to use compared to NVivo because it has fewer features that are easy to use.
However, the main difference between the two is that NVivo is mainly designed for research. The matrix table provides the user with a quick view of the data patterns formed. It provides the user with both the analyzed data ready for printing and data in softcopy stored in the database. It also provides the user with links that can be used for further searches in the future that relates to the document or any other document, (Salvendy, 1992). This feature is useful when writing things like memos or arguments that require one to support his or her views. ATLAS, on the other hand, is most famous as a network implement. It provides the user with many connections between the different codes that need to be analyzed. Its utmost strength is in providing connection within the text itself, not with any external source. This gives the user an opportunity to differentiate between contradicting and persuasive statements.
CAQDAS (Computer Assisted Qualitative Data Analysis Software (CAQDAS) Networking Project) is more efficient compared to NVIVO. This is because it does not use up most of the CPU time when compared to NVIVO. Some of the computer software’s freeze when put under much pressure. However, CAQDAS holds on for a long while due to its ability to use only a small portion of the time the CPU allocates it. It can handle vast amounts of data efficiently because it has the capabilities of performing tasks in a mix and match way. Its main function is the incorporation of data in qualitative form into quantitative and vice versa, (Mertins, & Krause & Schallock 1999).
Both CAQDAS and NVIVO analyze the repetition of codes in the text under analysis. NVIVO displays this by using different colors to highlight the codes that are co-occurring. These colors get darker and darker as the codes continue recurring in the text. CAQDAS displays this feature by providing a visual display of the texts and codes that are co-occurring in the text.
Mixed methods are some of the advantages found in both CAQDAS and NVIVO. It includes the flexibility and handiness of the two soft wares. However, NVIVO is not that suited for the mixed method functioning especially where the data under analysis is of a large quantity. The best feature to use when applying the mix and match method when using CAQDAS is the N6. This is because it has the capacity of processing large volumes of text. Fitted in is an automatic program for processing data, activated by the scripting language.
I need to look for data that I need from different materials. After that, I will then keep records in an organized manner of what I have collected to prevent the data from being lost. The first thing I have to do is to list write down the places and sources where I have to find the data I looked for. Some of the places I am sure to find useful information are from the curse text: Qualitative Data Analysis: An Expanded Sourcebook. This only means that I have to look for the book either in the school library or online in advance. The next step is to organize the data I have found in a manner that would be easy to retrieve when the need arises. This is through preparing and coding the information using unique numbers or letters each representing a different subheading. This I will do by dividing the information into related topics then the next thing will be assigning information with similar information a common subheading. I will then consider the different software’s that I have to use in the data analysis of the text on which it has based the research (Salvendy, 1992). The software application that I will choose will depend on its speed, efficiency and flexibility. After completion, I will then code the information I have used the ATLAS software. This will help identify any errors in the text, and it will also save time. It will also be valuable in coding the text ensuring that work is well stored and ready for printing.
Bronfenbrenner, K. (1998).Organizing to win: new research on union strategies.. Chicago: Tyndale House Publishers.
Bryman, A. & Hardy, M. (2009).Handbook of Data Analysis. London: Free Spirit Publishing.
Marzano, J. R. & Pickering, D. (2001).Classroom instruction that works: research-based strategies. New York: Intervarsity Press.
Mertins, K. & Schallock, B. (1999).Global production management: IFIP WG5.7 International Conference.
Oman, W.P. & Pfleeger, S. L. (1997). Applying software metrics. Maryland: Land Pub.
Salvendy, G. (1992). Handbook of industrial engineering.Chicago: Snippet view.