Analysing Quantitative Data

Quantitative data can be analysed through the application of a body of methods and theory known as ‘statistics’. These methods enable the researcher to analyse and interpret the data that has been collected in order to obtain the information required to provide answers to the research questions and address the research objectives. Although quantitative data analysis begins after the data have been collected, it is important for the researcher to be fully aware of the techniques available at a fairly early stage. This is because the techniques have to be appropriately matched to the types of variable that have been created through the research. The size and nature of the sample for the study can also impose limitations on the kinds of techniques that can be used by the researcher.

Zikmund (2003) provided an overview of the stages in the data analysis process for quantitative research:

• Editing: This is the process of preparing the data for coding and transfer to data storage by checking and adjusting the data for omissions, legibility and consistency. The aim of editing is to ensure completeness, consistency and reliability of the data collected.

• Coding: This is the process of identifying and classifying the answers with numerical scores or other character symbols in preparation for the transfer of the

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survey data to computers. Codes are numerical symbols used for interpreting, classifying and recording data.

• Data entry: This is the process of transferring the data gathered for the research study onto a computer system. Research studies using self – administered web surveys or computer – assisted telephone interviewing allow responses to be automatically stored and tabulated as they are collected and can reduce the clerical errors that often occur during the editing and coding process.

• Data analysis: This is the process of transforming the raw data into the information required to address the research questions. The interpretation of the data can be done through descriptive analysis, univariate analysis, bivariate analysis and multivariate analysis. Descriptive analysis entails the transformation of the data into a form that will make them easy to understand and interpret by providing descriptive information. Univariate analysis assesses the statistical significance of a hypothesis about a single variable. Bivariate analysis is used for the investigation of two variables using tests of differences or measures of association between two variables at a time. Multivariate analysis allows the researcher to simultaneously investigate more than two variables. The data collected through the self – completion questionnaire was entered into SPSS Version 20 which has the ability to perform a range of statistical tests and also generate tables, charts and graphs that can be used to present the data. The objective of the data analysis process is to provide descriptive and inferential statistics that will enable the researcher to properly address the research questions. Descriptive statistics help to summarise, describe and display quantitative data, whilst inferential statistics help to draw conclusions about a population from quantitative data relating to a random sample. Inferential statistics enable the researcher to reach conclusions that extend beyond the data and are used to infer, based on the study of a sample of a population, what the entire population might think or do (Quinlan, 2011).

Most of the data collected through the self – completion questionnaires are categorical rather than numerical data. Categorical data are those whose values cannot be measured numerically but can be classified into sets according to the characteristics identified or placed in rank order. Numerical data refers to data whose values can be measured or

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counted numerically as quantities. The table below shows some of the ways in which the data can be presented for analysis:

Data Type Data Presentation

To show one variable so that any specific value can be read easily

Table / frequency distribution To show the frequency of occurrences of categories or

values for one variable so that highest and lowest are clear

Bar chart or pictogram To show the trend for a variable Line graph or bar chart To show the proportion of occurrences of categories or

values for one variable

Pie chart or bar chart To show the distribution of values for one variable Frequency polygon To show the interdependence between two or more

variables so that any specific value can be read easily

Contingency table / cross – tabulation

To compare the frequency of occurrences of categories or values for two or more variables so that the highest & lowest are clear

Multiple bar chart

To compare the trends for two or more variables so that conjunctions are clear

Multiple line graph or multiple bar chart

To compare the proportions of occurrences of categories or values for two or more variables

Comparative pie charts or percentage component bar chart To compare the distribution of values for two or more

variables

Multiple box plot To compare the frequency of occurrences of categories or

values for two or more variables so that totals are clear

Stacked bar chart To compare the proportions and totals of occurrences of

categories or values for two or more variables

Comparative proportional pie charts

To show the relationship between cases for two variable Scatter graph / scatter plot

Table 4.7: A summary of data presentation by data type (Saunders et al, 2012, pp.489). The data from the questionnaires were entered into an SPSS Version 20 file created by the researcher and checked for errors. The researcher then used descriptive analysis on SPSS to transform the data into different forms so that they can be easily interpreted and understood when reported in the presentation of findings section. The low number of respondents and the broad groupings of responses to most of the questions made it difficult for the researcher to use inferential statistics to draw sensible conclusions from the quantitative data collected through the survey.

The researcher was later advised to combine some of the answer groupings in order to be able to conduct more sophisticated analysis of the data to provide better insight into the issues being examined by the study. The researcher combined the four age groups into two and also combined several similar groups of answers on initial information source, reason for choosing the university and most influential interpersonal source in

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order to restructure the data for further analysis. This enabled the researcher to cross- tabulate some of the responses and conduct Chi – square tests.