Explaining & Comparing Quantitative and Qualitative Methods
Quantitative Method
Quantitative research methods describe and measure the level of occurrences on the basis of numbers and calculations. Moreover, the questions of “how many?” and “how often?” are often asked in quantitative studies.
Quantitative studies mainly examine relationships between numerically measured variables with the application of statistical techniques.
Quantitative Data Analysis
In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking.
Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually associated with finding evidence to either support or reject hypotheses you have formulated at the earlier stages of your research process.
The same figure within data set can be interpreted in many different ways; therefore it is important to apply fair and careful judgment.
Quantitative data analysis with the application of statistical software consists of the following stages:
- Preparing and checking the data. Input of data into computer
- Selecting the most appropriate tables and diagrams to use according to your research objectives
- Selecting the most appropriate statistics to describe your data
- Selecting the most appropriate statistics to examine relationships and trends in your data
Quantitative Data Analysis Software
A set of analytical software can be used to assist with analysis of quantitative data, The following are the most three popular quantitative data analysis software:
- Excel Spreadsheet
- Microsoft Access
- SPSS
Qualitative Data Analysis
Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. Qualitative data analysis can be divided into the following five categories:
- Content analysis — This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data.
- Narrative analysis — This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher.
- Discourse analysis — A method of analysis of naturally occurring talk and all types of written text.
- Framework analysis — This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation.
- Grounded theory — This method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory.
Qualitative Data Analysis Steps
Qualitative data analysis can be conducted through the following three steps:
Step 1: Developing and Applying Codes
Coding can be explained as categorization of data. A ‘code’ can be a word or a short phrase that represents a theme or an idea. All codes need to be assigned meaningful titles. A wide range of non-quantifiable elements such as events, behaviours, activities, meanings etc. can be coded.
Step 2: Identifying Themes, Patterns and Relationships
In qualitative data analysis there are no universally applicable techniques that can be applied to generate findings. Analytical and critical thinking skills of researcher plays significant role in data analysis in qualitative studies.
Therefore, no qualitative study can be repeated to generate the same results. Nevertheless, there is a set of techniques that you can use to identify common themes, patterns and relationships within responses of sample group members in relation to codes that have been specified in the previous stage.
Step 3: Summarizing the Data
At this last stage you need to link research findings to hypotheses or research aim and objectives. When writing data analysis chapter, you can use noteworthy quotations from the transcript in order to highlight major themes within findings and possible contradictions.
It is important to note that the process of qualitative data analysis described above is general and different types of qualitative studies may require slightly different methods of data analysis.
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Brief Comparison
Requirement:
| Quantitative | Qualitative | |
|---|---|---|
| Question | Hypothesis | Interest |
| Method | Control and randomization | Curiosity and reflexivity |
| Data collection | Response | Viewpoint |
| Outcome | Dependent variable | Accounts |
| : | Question | |
| Quantitative: | Hypothesis | |
| Qualitative: | Interest | |
| : | Method | |
| Quantitative: | Control and randomization | |
| Qualitative: | Curiosity and reflexivity | |
| : | Data collection | |
| Quantitative: | Response | |
| Qualitative: | Viewpoint | |
| : | Outcome | |
| Quantitative: | Dependent variable | |
| Qualitative: | Accounts |
Ideal:
| Quantitative | Qualitative | |
|---|---|---|
| Data | Numerical | Textual |
| Sample size | Large (power) | Small (saturation) |
| Context | Eliminated | Highlighted |
| Analysis | Rejection on null | Synthesis |
| : | Data | |
| Quantitative: | Numerical | |
| Qualitative: | Textual | |
| : | Sample size | |
| Quantitative: | Large (power) | |
| Qualitative: | Small (saturation) | |
| : | Context | |
| Quantitative: | Eliminated | |
| Qualitative: | Highlighted | |
| : | Analysis | |
| Quantitative: | Rejection on null | |
| Qualitative: | Synthesis |
Three Types of Coding
There are three types of coding:
- Open coding — The initial organization of raw data to try to make sense of it
- Axial coding — Interconnecting and linking the categories of codes
- Selective coding — Formulating the story through connecting the categories
