Data Collection For Research Papers

Data Gathering Procedure for Research Papers

During our time, dissertation writing is perceived to be a simple research process converted to a narrative essay. It is true that writing such a project is easy if you have the right sources but one of the most problematic aspects in writing a dissertation paper is the aspect of data gathering procedure. Usually, it involves statistics and knowledge about using it. This is the major dilemma of students when it comes to research paper writing.

There are different data gathering procedures that you can use for a term paper. It will depend on your convenience and the ease in gathering the required details that you can use for the research results. In any case, we will discuss some of the major types of data gathering procedures.

Data Mining-this procedure is simple. You can easily find many resource materials where data and numerical figures are already tallied and presented. Usually, you can find data among research institutions and academic centers. Data mining is simply using an already published set of data.

Interviewing-is another example of a data gathering procedure. One benefit that you can get from this process is that you can actually gather raw and reliable data direct from your subjects. Although it is time consuming, you can still have very credible result from this data gathering procedure.

Surveying-actually involves gathering responses from subjects through a written medium. You will be distributing paper questionnaires for your subjects to answer. You will then tally the results and use the data for your research. You may find an essay online about how to write questionnaires.

These are just some of the basic data gathering procedures that you can utilize for your research paper. Send us any inquiries about other concerns in writing a dissertation paper.

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In your research proposal, you will also discuss how you will conduct an analysis of your data.  By the time you get to the analysis of your data, most of the really difficult work has been done. It's much more difficult to define the research problem, develop and implement a sampling plan, develop a design structure, and determine your measures. If you have done this work well, the analysis of the data is usually a fairly straightforward affair.

Before you look at the various ways of analyzing and discussing data, you need to review the differences betweenqualitative research/quantitative researchandqualitative data/quantitative data.

Why do I have to analyze data?

The purpose of analyzing data is to obtain usable and useful information. The analysis, regardless of whether the data is qualitative or quantitative, may:

  • describe and summarize the data.
  • identify relationships between variables.
  • compare variables. 
  • identify the difference between variables.
  • forecast outcomes. 

Earlier, you distinguished between qualitative and quantitativeresearch. It is highly unlikely that your research will be purely one or the other – it will probably be amixtureof the two approaches.

For example, you may have decided to ethnographic research, which is qualitative. In your first step, you may have taken a small sample (normally associated with qualitative research) but then conducted a structured interview or used a questionnaire (normally associated with quantitative research) to determine people’s attitudes to a particular phenomenon (qualitative research). It is therefore likely that your mixed approach will take a qualitative approach some of the time, and a quantitative approach at others depending on the needs of your investigation.

A source of confusion for many people is the belief that qualitative research generates just qualitative data (text, words, opinions, etc) and that quantitative research generates just quantitative data (numbers). Sometimes this is the case, but both types of data can be generated by each approach. For instance, a questionnaire (quantitative research) will often gather factual information like age, salary, length of service (quantitative data) – but may also collect opinions and attitudes (qualitative data).

When it comes todata analysis, some believe that statistical techniques are only applicable for quantitative data. This is not so. There are many statistical techniques that can be applied toqualitativedata, such as ratings scales, that has been generated by aquantitative research approach.  Even if a qualitative study uses no quantitative data, there are many ways of analyzing qualitative data. For example, having conducted an interview, transcription and organization of data are the first stages of analysis. This would then be continued by systematically analyzing the transcripts, grouping together comments on similar themes and attempting to interpret them, and draw conclusions.

1. Manchester Metropolitan University (Department of Information and Communications) and Learn Higher offer a clear introductory tutorial to qualitative and quantitative data analysis through their Analyze This!!! site. In additional to teaching about strategies for both approaches to data analysis, the tutorial is peppered with short quizzes to test your understanding. The site also links out to further reading. 

Complete this tutorial and use your new knowledge to complete yourplanning guide foryour data analysis.

There are many computer- and technology-related resources available to assist you in your data analysis.

Online General Resources

Quantitative Data Analysis Resources

  • The Rice Virtual Lab in Statistics also houses an online textbook, Hyperstat. This textbook introduces univariate and bivariate analysis, probability, distribution and hypothesis testing. The site also includes a really useful section of case studies, which use real life examples to illustrate various statistical tests.
  • Not sure which statistical test to use with your data? The University of the West of England has a really helpful tree diagram to help you decide which is the best one for you. The diagram is housed within another good introduction to data analysis.
  • Free Statistical Analysis and Data Management software

Common Computer-Aided Qualitative Data Analysis Packages

There are many computer packages that can support your qualitative data analysis. The following site offers a comprehensive overview of many of them: Online QDA

ATLAS.ti

Another package that allows you analyze textual, graphical, audio and video data. It also has the functionality to produce tag clouds to represent the codes you have created. No free demo, but there is a student price.

MAXQDA

This has add-ons which allow you to analyze vocabulary and carry out content analysis. There is a visual MAPS add-on which produces visual representations of your data. There is a free demo.

The Ethnograph

This was one of the first packages for qualitative data analysis. It allows you to create code trees and search your data easily. There is a free demo.

Transana, HyperRESEARCH and HyperTRANSCRIBE,  and N-Vivo are other products.

2.  When you are done, you will also need to address concerns about the reliability and validity of your possible results.  Use these questions and explanations for ideas as you complete your planning guide for this section.

Some common worries amongst researchers are:

  • Will the research I’ve done stand up to outside scrutiny?
  • Will anyone believe my findings? 

These questions are addressed by researchers by assessing the data collection method (the research instrument) for itsreliabilityand itsvalidity.

Reliability

Reliability is the extent to which the same finding will be obtained if the research was repeated at another time by another researcher. If the same finding can be obtained again, the instrument is consistentor reliable.

Validity

Validity is understood best by the question: ‘Are we measuring what we think we are measuring?’ This is very difficult to assess. The following questions are typical of those asked to assess validity issues:

  • Has the researcher gained full access to the knowledge and meanings of data?
  • Would experienced researchers use the same questions or methods?

No procedure is perfectly reliable, but if a data collection procedure is unreliable then it is also invalid.  The other problem is that even if it is reliable, then that does not mean it is necessarily valid.

Triangulation

Triangulation is crosschecking of data using multiple data sources or using two or more methods of data collection. There are different types of triangulation, including:

  • time triangulation– longitudinal studies
  • methodological triangulation – same method at different times or different methods on same object of study
  • investigator triangulation– uses more than one researcher.

Sampling error

Sampling error is a measure of the difference between the sample results and the population parameters being measured. It can never be eliminated, but if random sampling is used, sampling error occurs by chance but is reduced as the sample size increases. When non-random sampling is used this is not the case.

Basic questions we need to ask to assess a sample are:

  • Is the sample random and representative of the population?
  • Is the sample small or large? 

Non-sampling error

All errors, other than sampling errors, are non-sampling errors and can never be eliminated. The many sources of non-sampling errors include the following:

  • Researcher error– unclear definitions; reliability and validity issues; data analysis problems, for example, missing data.
  • Interviewer error– general approach; personal interview techniques; recording responses.
  • Respondent error– inability to answer; unwilling; cheating; not available; low response rate.

This section was discussed in Elements of the Proposal, where there are many online resources, and you have reflective journal entries that will support you as you develop your ideas for reliability and validity in your planning guide.  In addition this writing tutorial specifically addresses the ways in which this can be explained in your research proposal.

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