When collecting primary data for a research project the types of data obtained fall into one of two categories, Quantitative or Qualitative data. Quantitative is easily measured, normally expressed in numeric form and will be a count or a value. Qualitative data is not easily measured and is normally less structured than Quantitative. The goal of Qualitative data is to describe a topic more than measure it, this is accomplished with opinions, reasons and motivations rather than numerical analysis. “Qualitative data brings you the details and the depth to understand the full implications.” (Cantieri, 2019) For a well-rounded set of data, a combination of both Quantitative and Qualitative should be obtained. When collecting data, it is important to utilise a range of methods available.
Questionnaires can be an excellent method of obtaining data for research as they can be both paper or online based, it is easy to record the data, inexpensive, suits the Likert scale well, can include both Quantitative or Qualitative questioning and are normally completed directly by the respondent which will reduce the likelihood of mistakes. This makes questionnaires an excellent option for collecting data as part of my action research. While questionnaires have become the de facto method for research and marketing, there are some challenges, such as if questions are omitted by the respondent, it can prove challenging to rectify unless immediately noticed. Another aspect is that there is no way of knowing if the respondent has understood the question or respondents’ answers may be interpreted incorrectly. These factors will have a significant impact on the reliability of the data collected. A further downside that can also be beneficial is that questionnaires are focused and lack nuance, while this will mean useful data is likely to be missed, it also results in large amounts of useless data not being included that would have required processing. Questionnaires also lack inclusivity, as there are limited options available for tailoring a questionnaire to suit individual needs.
Observations can be an excellent method for collecting data and are best suited to qualitative data as observations can provide unmatched detail into actions, behaviour, emotion and attitude of those being observed. This makes them ideal for data collection in situations when other methods would be unsuitable. Observations also situate the research directly in the situation, rather than someone else’s perception of the situation. They are also ideal for nonverbal communication or recording data that would never be included within a questionnaire or interview, possibly due to being damaging or embarrassing to those being observed. Relating to this kind of communication is that observations will never collect the thoughts of those being observed, even if they are willing to share. Another important factor to consider is inaccurate data as a result of either researcher bias or the action being observed changing as a result of the observation, known as the observer effect. Observations are also extremely time consuming and the analysis of data can be challenging, likely resulting in smaller sample sizes.
An excellent method of obtaining qualitative data is to utilise test results, these may be from an existing test that was already a requirement, or a test specifically designed for the purpose of obtaining data as part of the research. When the test is designed for the research, creating before and after tests can be an excellent method to gauge progress or improvement. Whilst using test results can provide excellent data relating to actual performance, there are several drawbacks relating to how the results have been obtained, for example, if an exam structure has been implemented the results will favour students that performed well in an exam, at this point the researcher must question if the testing method is valid and fit for purpose. Another important aspect to consider, especially when using results from tests that are unrelated to the research, is whether it is ethical to make use of the data and has the student provided permission.
As the interviewer is face to face with the interviewee it makes it more difficult for the interviewee to lie during questioning, especially when it comes to personal information, such as age or gender. An interview provides the best method of collecting both verbal and non-verbal cues, allowing for unmatched levels of qualitative data collection. The ability of the interviewer to collect suitable information will have a large impact on the level of useful information that is collected. A good interviewer will ensure the interview remains focused, time bound, to ensure the interview doesn’t impact on other interviews, and that emotions and behaviours as well as comments are captured. Interviews are extremely time consuming and will result in large quantities of qualitative data, this is likely to limit the sample size. It is normal for data to be collected with the use of a Dictaphone that will require manual data entry at a later date in order for the data to be analysed.
Ways in Which Collected Data may be Analysed
Quantitative data can be analysed using two main methods, descriptive analysis and inferential analysis. Descriptive analysis is used to summarise variables and find patterns using methods such as average (Mean, Median and Mode), Percentage and Range. Inferential statistics rely on more complex analyses and are used to show relationships between variables in order to make predictions and gain deeper understanding. Inferential analysis makes use of methods such as correlation, which describes two variable’s relationship, regression analysis, in which the strength of the relationship is analysed and variance analysis, whereby the variable’s divergence from the average is analysed.
When displaying information that has been obtained from analysing quantitative data it is common to make use of Histograms, Stemplots, Bar graphs, Pie Charts, Line Graphs or Scatter diagrams.
When analysing qualitative data an alternative approach must be deployed, the first step once all the data has been transcribed is to organise the data. This organisation will most likely be into tables or an alternative ordered manner. Once this has been completed, the data must be categorised into “concepts, properties and patterns” (Electric Paper Ltd, 19) this stage will “give meaning to data collected” (Electric Paper Ltd, 19). When analysing Qualitative data some techniques can be utilised, such as theming, in which themes are identified by grouping data points. Coding, in which labels are attached to specific pieces of data, allowing comparison of similar datasets. Indexing, whereby a list of words of phrases is created, along with the location of the associated data, the phrase is selected based on the frequency and importance of the phrase within the dataset. A selection of methods can be used to display qualitative information beyond a typical analysis report; these include Word Clouds, in which key phrases are grouped together with the font size increasing based upon the frequency of the word’s usage. Word frequency bubbles, whereby key phrases are displayed in individual bubbles, the bubble size varies depending upon the frequency of the word’s usage. Timelines that display interviewee’s key comments or observee’s key actions over a period of time. Venn Diagrams that can be used to highlight where themes or indexes overlap, greater detail can be shown with the use of network maps that show the relationship between themes, concepts or indexes. A very effective method of displaying qualitative data is to show a single poignant student comment, ideally with their name. When the correct comment is selected that provides a summary of the required message, this can be a very powerful method of communicating qualitative data.
Cantieri, R. (2019, 06 09). The Difference Between Quantitative vs. Qualitative Research. Retrieved from Survey Monkey: https://www.surveymonkey.com/mp/quantitative-vs-qualitative-research/
Electric Paper Ltd. (19, 06 10). How to Effectively Carry Out a Qualitative Data Analysis. Retrieved from Achievability: https://www.achievability.co.uk/evasys/how-to-effectively-carry-out-a-qualitative-data-analysis
My first experience of teaching was in 2016, when I was asked to
deliver a talk to a group of 16-year-olds on what it was like to start
your own business. I immediately knew I wanted to become more
involved in teaching but I didn’t know where to start as I had not
previously considered a career in education. A few weeks later I
agreed to teach a class of Chinese students from the Shanghai
Technical Institute of Electronics and Information, who had travelled
to the UK to learn English and Software Engineering, after that I was
hooked. Within the next few years, I taught hundreds of students of
many different nationalities, aged from 16 to 60, and from
levels 2 to 6. I focused my time teaching with Bath University and
Bath College for several more years until I felt a change was in order.
For the last few years, I have taught remotely with several private
training organisations, provided dedicated one to one coaching
sessions, provided consultancy on teaching and assessment practices
and written about my experiences as a teacher. I plan to continue
with my current activities for the foreseeable future but I’m always
open to new teaching experiences.
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