Week 30 notes

Looking behind the data

We have briefed the importance of data analysis at the ‘Learn’ phase of Spiral of Inquiry (week 24), and you have been collecting data for your Teacher Inquiry since you started the ‘Take Action’ phase (week 25). Now, before you can evaluate the impacts of your Teacher Inquiry, you need to thoroughly reflect on what the data that you get from previous phases of your Inquiry can reveal, how and why?

‘Data’ and ‘Evidence’ in Your Teacher Inquiry

Shaddock (2013) defined data as basic units of information that are ‘givens’ or ‘factual’. When data is processed for a certain purpose it become evidence.Evidence, rather than data, is then used as the basis for decisions, since it is data being used to support a conclusion. In educational research, for example, one student's grades, or one interview with a colleague are considered data while the grades of a whole class, or a group of interviews, are evidence, once suitably analysed.

The whole Spiral of Inquiry cycle should involve data and evidence, you gather and analyze. Interpret the data first, and then use the evidence processed from data to support the improvement in your practice. “Reflect on Evidence” is the last step of our “Take Action” phase of the Spiral of Inquiry, it is also a critical step that can decide how you use the evidence to evaluate the impacts of your Teacher Inquiry in the ‘Check’ phase.

Data Analysis and Interpretation

“The process of analysis is defined as breaking down the whole into elements in order to discover its essential features. Interpretation means providing a description of the meaning of the study” (Efron and Ravid, 2013, p165).

As we briefed in week 24, Efron and Ravid (2013) propose the following 3 key steps for research data analysis process: Preparation for analysis (Step 1), Analysis of the data (Step 2) and Interpretation of the data (Step 3). Each of these are detailed below.

Step 1: Preparation for analysis

Preparing qualitative data

Sorting and familiarizing the data

The transformed data such as typed text and digital copies can be arranged into different files based on participants (such as parents, students etc) or data collection methods (such as survey, observation) then you can create data file organizers such as tables to organize data, finally, you should get the overall sense of the data by reading and reread the data.

Preparing quantitative data

Cleaning and formatting data

Before you start to analyze data, it is essential to make sure that the data with which you are working is "clean". This means that it is consistent, accurate and complete. Any missing values, duplications of data, and incorrect information need to be filed, removed, and corrected before you start analyzing the data.

Some survey tools such as SurveyMonkey and Google Forms can export directly into Excel or CSV formats. You should also make sure that each of your variables is in the right number format, such as dates.

The TKI website has this Data Analysis page which gives advices on manipulating data and preparing it for analysis.

Step 2: Analysis of the data

In week 23, we discussed different data collection methods. Depending on the methods you plan to use, the data that you collected can be quantitative or qualitative or both.

Analysis of the qualitative data

As we introduced in week 24, ‘Coding’ is a very helpful approach which can be used to analyze qualitative data.

In general, the coding categories emerge from the data or may be predetermined by you before you analyse the data or both. You can draw the categories from your research question or from the literature that you have reviewed.

You can find examples of code categories from Babione (2015, pp. 142-3) (required) for your data analysis or create a different category to fit the need of your inquiry.

In part 2 of her book, Babione (2015) has compiled a number of actual research studies conducted by different practitioners. One of the teacher inquiry projects is an initiative on one-on-one iPads. The data collection methods include interviews with educators and students as well as surveys of parents and students. You can read p.255-260 of Babione (2015) (supplementary) to see how the researcher analysed the quantitative data from survey responses and qualitative data from interview and written survey responses.

Analysis of quantitative data

Descriptive statistics help to illustrate and summarise data. There are a number of calculations to explore the data depending on what you want to know, this guide to quantitative calculations summarises when to use frequency, mean, median, percentage, standard deviation. How you explore the data depends on what you want to know. Here’s some examples of ways to explore quantitative data. There is also advice on how to read and analyse data with illustrated graphs from TKI website.

Step 3: Interpretation of the data

Interpretation of data provides a narrative of how the analysed data answers the research questions. Gray (2012) in the Guardian in ’What data can and cannot do’, challenges the assumption that ‘data is a perfect reflection of the world’ because it is up to the individual to interpret and give meaning to a set of data.

In this tutorial video (supplementary), Margaret Riel walks through analyzing data for action research, from breaking down the research question to organising collected data and making sense of it.

Interpretation of qualitative data

In qualitative data, the different categories are grouped into an encompassing one to narrate a more holistic story to address the research question. You can see more details of how to identify patterns in data in Efron and Ravid (2013, pp 177-9) (supplementary).

Interpretation of quantitative data

In quantitative data, different numerical results are added up together to test the hypotheses or the research question. Efron and Ravid (2013, pp. 205-7)(supplementary) discuss important aspects of evaluating statistical data and making meaning of them.

The readings in this part provide helpful information in terms of how analysed data can be put together to form the answer of the research question. Understanding data analysis can also help you to anticipate what the research or inquiry could potentially reveal or indicate. For example, researchers need to be careful in generalising the results to a wider population.

THIS WEEK’S ASSESSMENT ACTIVITY - Reflect on Your Evidence

Activity 6: Create a reflective entry to describe the data you have collected so far and how you are analysing it.

Step 1: Describe the data you have collected

Describe the data that you have collected, for example, what qualitative and/or quantitative data have you collected? Why was this method of data collection appropriate for your research question? At what phase(s) of the Inquiry of Spiral was the data collected? Have you got all the data you planned to collect in your Action Plan? What data preparation have you done to get your raw data ready for analysis?

Step 2: Explain how you are analysing your data

If you collected qualitative data, you need to explain how you have used coding technique(s) to analyse your data. For example, if you have used predetermined categories, explain what they are and how they are formulated.

If you collected quantitative data, you need to explain which descriptive statistics techniques you have used to illustrate and summarise data. Also, if you collected data on a large scale across a syndicate or the whole school, you also need to explain how you have used the sample size calculator tool (week 23’s Class Notes) to verify the margin of error for the actual number of responses you receive if you plan to conduct a survey as one of your data collection methods. 

You may want to use tables, graphs, or flow chart etc. to present your data. Remember to link your explanation to relevant theories / resources and add references.

Step 3: Reflect on your evidence so far

After you analyzed the data, what evidence have you got to answer your Inquiry questions? To what extent have the question being answered? Are there anything that is not address by your data? What could you have done better in terms of data collection and analysis?

References

Babione, C. (2015). Practitioner Teacher Inquiry and Research. USA: John Wiley & Sons. (e-copy available in Unitec library).

Efron, S. E., & Ravid, R. (2013). Action research in education: A practical guide. New York, NY: The Guilford Press. (e-copy available in Unitec library).

Gray, J. (2012).What data can and cannot do. Retrieved from https://www.theguardian.com/news/datablog/2012/may/31/data-journalism-focused-critical

Schildkamp, K., Lai, M. K., & Earl, L. (2012). Data-based decision making in education: Challenges and opportunities. New York: Springer.