Page 112 - DLIS401_METHODOLOGY_OF_RESEARCH_AND_STATISTICAL_TECHNIQUES

Unit 7: Data Analysis and Interpretation

Do not analyse the data on a question-by-question basis. You should summarise the key Notes
themes that emerge from the data and may give selected quotes if these are particularly
appropriate.
By presenting we mean a factual description/summary of what you found. The discussion
element is your interpretation of what these findings mean and how they confirm or contradict
what you wrote about in your literature section.
If you are trying to test a model then this will have been explored in your literature review
and your methodology section will explain how you intend to test it. Your methodology
should include who was interviewed with a clear rationale for your choices to explain how
this fits into your research questions, how you ensured that the data was unbiased and as
accurate as possible, and how the data was analysed. If you have been able to present an
adapted model appropriate to your particular context then this should come towards the end
of your findings section.
It may be desirable to put a small number of transcripts in the appendices but discuss this
with your supervisor. Remember you have to present accurately what was said and what you
think it means.
In order to write up your methodology section, you are strongly recommended to do some
reading in research textbooks on interview techniques and the analysis of qualitative data.
There are some suggested texts in the Further Reading section at the end of this pack.

7.1.7 Quantitative Data Analysis

Here we are concerned with the basics of statistical analysis. However, we do not cover the
techniques in detail but provide a brief overview. If you are unsure of these or have forgotten
them, you should refer to your notes from previous studies or consult introductory statistics
textbooks. We begin by looking at some basic ideas about analysis and presentation of data.
These are ‘variables’ and the related idea of ‘scales of measurement’.
Variables
Constant reference is made in statistics textbooks to the term variable. A variable is a characteristic
of interest that varies from one item to another and may take any one of a specified set of
values or attributes. Variables are usually classified as quantitative or qualitative. For example,
consider a study of guests at a hotel. We may be interested in the age of a guest, their spend
and length of stay. Each characteristic is a quantitative variable because the data that each
generates is numerical – for instance, a guest may be 34 years of age, spend £500 and stay for
seven days. Quantitative variables generate quantitative data.

Notes Qualitative variables generate non-numerical or qualitative data. For instance, ‘nationality
of hotel guest’ is a qualitative variable because nationality can be classified as
British, American, French, etc.

Scales of measurement
Many people are confused about what type of analysis to use on a set of data and the relevant
forms of pictorial presentation or data display. The decision is based on the scale of measurement
of the data. These scales are nominal, ordinal and numerical. (Strictly numerical can be sub-
divided into interval and ratio – however, we do not draw that distinction here.) Inferential
analysis in which a decision is made about whether the particular results of the study are
“real”. More emphasis will be placed on descriptive analysis in this fact sheet.

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