How to Organize Appropriate Data

How to Organize Appropriate Data

In short, here are 5 easy steps to organize your quality data

  1. Select and follow a detailed file naming program

  2. Create a data tracking system

  3. Establish and document writing / translation processes

  4. Establish quality control procedures

  5. Set a Virtual Timeline

Before we take a closer look at 5 steps, what is quality data?

Organizing and presenting dataQuality data is information that separates but does not measure features or features of existence. Quality data provides meaning and not definition.

The main categories of quality data are in-depth discussions, focus group discussions, direct observations and written texts. In any case, quality data has words and can be more painful than a service if not properly organized.

The collection of relevant research data begins with the recording of high quality audio (see this great post for choosing the best voice recorder for research interviews).

Then, check all your data to make sure everything is accurate and compact. For example it is important to review the literature for your research discussions before integrating and editing them.

Finally, researchers analyze their data and use their findings to inform their thesis / outline, essays, reports, and books.

Here are 5 steps to keeping your bulk data organized.

5 Steps to Organizing Your Appropriate Data

  1. Select and follow a clear file naming program

Identification is key.

Provide your personal data, name your data. Depending on the system you are using, categorize your data. You can also combine related themes into larger categories and label them. And what better way to identify your data than to choose and follow a clear file compiling plan?

In this way your information will be properly integrated, easily accessible and functional.

For example, if the study was about the tendency to drink among men between the ages of 12 and 18, you could use the age group as a file name. Age 12 to Age 18 will therefore make the names of 7 files, including all that was found in that particular age group.

Work with what works on your data. All data categories must be identifiable. Coding can be another option, where you can use tags to identify files with it.

  1. Create a data tracking system

In large projects data collection and management involves the integration of multiple actors into a research team. From sources, to dates, interviewer, interviewer, consultant, writer, translator (per diem) and even sites, depending on the survey.

There is a need for a consistent flow of accounts and events so that there is no conflicting response due to merging.

You need to create a system that helps track all the data collected, from field work to aggregation, all involving sources and context. It is important to set up a custom tracking system on detailed data of people, sites and dates.

  1. Establish and document writing / translation processes

After data tracking, transcription processes should be established as part of the planning process. Formally, recorded data needs to be recorded. There are a few questions you need to answer for more information.

  • What format will be used for writing? Verbatim? Intelligent Verbatim? (Note that some research work requires one or more)
  • Is there a translation required? Do you find multilingual writers or work with translators and writers? How do you ensure that information is not distorted during translation?
  • What consensus formats will you develop? (signs, structures, spaces)
  • Confidentiality procedures (What should your registrar know? Do interview participants need protection? Do you need to sign Non-Disclosure Agreements (NDAs))
  • What is the time allotted for writing?
  • Do you outsource professional writing services or will you hire your full-time or part-time employees? (Depending on the nature and consistency of quality data flow, one might consider having your own staff. But in one combination, outsourcing is the solution)
  • Is any training required? (What is your budget? What are your options?)
  • What corrective measures are required for quality purposes? (Is the translation accurate? Are the instructions followed? Is there anything left out?)
  • Are any instructions required? (Are there parts that need to stay as they are, no translation, no editing? Is there something that needs to be left out?).

Once all the above questions have been answered, an instruction will be established on how to organize the research recording procedures.

As of writing, translation is time consuming, requires quality monitoring and is very expensive. One of the major problems with translation is distortion. Sorting data using the questions above as key indicators will ensure that you maintain consistency in your research.

  1. Establish quality control procedures

As shown above there is much to consider. As soon as the recording begins, a testing system should be created for quality control purposes. A guide that will help monitor the whole process. The recording should be compared to the record and regular contact with the author will produce better results. This way it can be easier to catch mistakes in time and provide guidance as needed.

  1. Set a real timeline

Set a real timeline. Plan your time, your strategy, and your needs. Have a plan of action. Have a comprehensive plan for how you want to manage your data and work on it systematically.

If there is a need to pull out, plan all the activities involved to make sure you have everything you need when you need it. Make sure you create real and tangible moments. There is no such thing as a time-saving, efficient, and intelligent approach

With all that said and done, it would be wise to make a copy of your work for safekeeping. Make a backup copy, make a backup copy and make an additional backup copy, the gold rule for research work. Copying and archiving can save lives; you may not know when this simple act will be sent to heaven.

 

Editing your quality data should be a way to view collection and analysis. Following the steps outlined above prevents confusion from data collection, to analyzing and writing your research book.

 

Example of a Data Management Process

 

Here is an example of an interview data management process.

 

>. Make a sound backup on the hard drive.

>.Rename the file and create a list in the main spreadsheet.

>.Edit the audio file (if required).

>.Save the audio file to the normal task folder.

>Assign writing tasks.

>.Create a text file and save it with the same name as the audio file.

>.The first author of the first draft.

>.Re-visit problem areas for first fixes.

>.To check spelling, check the original draft format.

>.Second writing test.

>.Replace or specify (if required).

>.The second author repeats the steps of the first author.

>.The main writer checks the format, number of words, length etc.

.Writing file is locked for review.

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