How to analyze your data Deciding which approach to use Analysing qualitative data Analysing quantitative data Measuring data

Deciding which approach to use The method you use to analyse your data will depend on whether you have chosen to conduct qualitative or quantitative research, and this choice will be influenced by methodological preference and educational background. For quantitative data analysis, issues of validity and reliability are important. Qualitative data analysis is a very personal process.

When to analyse data For qualitative data, the researcher might analyse as the research progress, continually refining and reorganizing in light of the emerging result. For quantitative data, the analysis can be left until the end of the data collection process, and if it is a large survey, statistical software is the easiest and most efficient method to use.

Analysing qualitative data To help you with the analysis of qualitative data, it is useful to produce an interview summary form or focus group summary form which you complete as soon as possible after each interview or focus group has taken place. Format for analysis: This might be a transcript from an interview or focus group, a series of written answers on an open-ended questionnaire, or field motes or memos written by the researcher.

Types of qualitative data analysis 1.Thematic analysis: Data is analyzed by theme, and it is highly inductive that means the themes emerged from the data and are not imposed upon it by the researcher. Background reading can form part of the analysis process in explaining an emerging theme. Look at example 8 page 120

2. Comparative analysis Data from different people is compared and contrasted and the process continues until the researcher is satisfied that no new issues are raising. Example 9 page 121

3. Content analysis Using this method, the research systematically works through each transcript assigning codes, which may be numbers or words, to specific characteristics within the text. Example

4. Discourse analysis Discourse analysis or conversational analysis look at patterns of speech, such as ho people talk about a particular subject, what metaphor they use, how they take turns in conversation, and so on These analysts see speech as a performance; it performs an action rather than describes a specific state of affairs or a specific state of mind. Example 11 page

Analysing quantitative data 1.Computing software If you have computing software available for you to use you should find this the easiest and quickest way to analyse your data. However, data input can be a long and laborious process, especially for those who are slow on the keyboard, and if any data is entered incorrectly, it will influence the your results.

Statistical techniques For those who do not have access to data analysis software, a basic knowledge of statistical techniques is needed to analyse your data. If your goal is to describe what you have found, all you need is to count your responses and reproduce them. This is called a frequency count or univariate analysis See table 11& example 12 page 128

Finding a connection Although frequency counts are useful starting point in quantitative data analysis, you may find that you need to do more than describing your findings. Often you will need to find out if there is a connection between one variable and number of other variables. For example, a researcher might want to find out whether there is a connection between watching violent films and aggressive behavior. This is called bivariate analysis.

In multivariate analysis the researcher is interested in exploring the connections among more than two variables. For example, a researcher might be interested in finding out whether women aged 40-50, in professional occupations are more likely to try complementary therapies than younger, non professional women and men from all categories.

Measuring data 1. Nominal scales: In nominal scale, the respondent answers a question in a particular way, choosing from a number of mutually exclusive answers. Answers to questions about martial status, religious affiliation and gender are examples of nominal scales of measurements.

2.Ordinal scales: Some questions offer a choice but from the categories given, it is obvious that the answers form a scale. They can be placed on continuum, with the implication being that some categories are better than others.

3. Interval scales They come in the form of numbers with precisely defined intervals Examples include the answers from questions about age, number of children and household income. With an interval scale, you know not only whether different values are bigger or smaller, you also know how much bigger or smaller they are. For example, suppose it is 60 degrees Fahrenheit on Monday and 70 degrees on Tuesday. You know not only that it was hotter on Tuesday, you also know that it was 10 degrees hotter.