STAGE 5: Analysing and Interpreting Data | Centre for Teaching Excellence

Data Analysis

The methods for analysing your data depend on your research question, guiding approach, data collection methods, and type of data collected. Consequently, your data analysis and presentation should align with these factors.

Similar to data collection, data analysis methods fall into two main categories: quantitative and qualitative.

Quantitative Data Analysis

You can make use of these statistical tests to handle quantitative data:

Hypothesis testing - to determine how likely a random data sample will detect an apparent effect by chance
Descriptive statistics - to generate statistical answers for easy interpretation
Statistical inference - using a random sample to infer the properties of a whole population
Causal analysis - to determine whether there is a causation
Correlation coefficient - a measure that helps determine whether there is relationship between two variables
Analysis of variance (ANOVA) - a method to identify whether categorical variables have a statistically significant effect on a continuous variable
Correlation analysis - to examine the relationship between two continuous variables, and measures the strength and direction of their association

It is common for researchers to use various software to carry out statistical analysis. These are some common tools:

R Studio is free to download and relatively easier to use. There are extensive libraries for various statistical methods with open codes widely available. You can easily add your programme.
Excel is easy to use and user-friendly. It is best used for basic data analysis, visualization, and simple statistical functions. It is also easy to integrate with other Microsoft Office tools. It is suitable for simple, quick analyses but not so much for large datasets.
SPSS is popular for statistical analysis in social sciences, offering a range of advanced analytical techniques. It has an intuitive interface with many built-in statistical tests and procedures. However, it is not free to use and requires subscription.
Python is a powerful programming language with extensive libraries for data analysis and visualisation. It can handle a wide range of data analysis tasks. However, the learning curve is steep as it requires programming knowledge and initial setup, and library management can be complex.
Mini tab offers tools for basic and advanced analysis. It is user-friendly with good documentation. However, it is less comprehensive than R or Python for varied statistical needs and can be expensive.

For a detailed guide on analysing quantitative data, you can refer to:

Qualitative Data Analysis

Expand each section to learn more.

1. Concept Maps

What are concept maps?
Concept maps provide a graphical representation of relationships among concepts or ideas. As new data emerges, the concept map may evolve by potentially establishing fresh links, altering connections, and incorporating new concepts.
Strengths	Limitations
Visual identification of themes and patterns Facilitate the process of cross-group analysis, making it easier to compare similarities or highlighting differences Allows the researcher to reduce the data in a meaningful way They can help in identifying gaps in knowledge or understanding	For large data sets, concept maps can become cluttered and difficult to manage Creation of concept maps can be subjective depending on the individual's perspective and understanding Risk of oversimplifying complex relationships, potentially missing out on nuanced details

More Detailed Guides

2. Content Analysis

What is content analysis?
Content Analysis involves systematically analyzing textual, audio, or visual content. It is a research technique for making replicable and valid inferences from texts (or other content) to the contexts of their use. The purpose of content analysis is to describe the characteristics of the document's content by examining who says what, to whom, and with what effect (Bloor & Wood, 2006). Content analysis is widely used in educational research to explore various aspects of teaching, learning, curriculum, and educational policies. For example, it is used to analyze transcripts of classroom interactions, including teacher-student dialogues, peer interactions, and discussions, to understand patterns of communication, teaching strategies, and student engagement.
Strengths	Limitations
Allows for systematic analysis of large amounts of data and can cover implicit meanings to provide insights into context and social phenomena.	Vulnerable to researcher bias in coding and interpretation Can be time-consuming May not capture non-verbal aspects of communication

What is content analysis?

Content Analysis involves systematically analyzing textual, audio, or visual content. It is a research technique for making replicable and valid inferences from texts (or other content) to the contexts of their use. The purpose of content analysis is to describe the characteristics of the document's content by examining who says what, to whom, and with what effect (Bloor & Wood, 2006).

Content analysis is widely used in educational research to explore various aspects of teaching, learning, curriculum, and educational policies. For example, it is used to analyze transcripts of classroom interactions, including teacher-student dialogues, peer interactions, and discussions, to understand patterns of communication, teaching strategies, and student engagement.

Strengths

Limitations

Allows for systematic analysis of large amounts of data and can cover implicit meanings to provide insights into context and social phenomena.

Vulnerable to researcher bias in coding and interpretation
Can be time-consuming
May not capture non-verbal aspects of communication

More Detailed Guides

3. Thematic Analysis

What is thematic analysis?
Thematic analysis is a type of content analysis. It involves identification, analysis, and interpretation of patterns/themes within data. Clarke and Braun (2013) propose a 6-step data analysis process: Using this method, you must familiarise yourself with the data collected before starting to code the data. This initial coding involves labelling and categorizing segments of the data that relate to the research question or topic of interest. From this, look for broader patterns or themes. Themes are then reviewed to ensure that they accurately reflect the data. After further refining and defining themes to reflect the data, you can write up the analysis to explain how these patterns provide insights or relate to the research question.
Strengths	Limitations
Flexible approach that enables one to generate new insights and concepts Accessible for novice researchers	Different ways to interpret data due to flexibility As it focuses on common themes across interviews, issues that arise from one single data set may be overlooked

What is thematic analysis?

Thematic analysis is a type of content analysis. It involves identification, analysis, and interpretation of patterns/themes within data.

Clarke and Braun (2013) propose a 6-step data analysis process:

Using this method, you must familiarise yourself with the data collected before starting to code the data. This initial coding involves labelling and categorizing segments of the data that relate to the research question or topic of interest. From this, look for broader patterns or themes. Themes are then reviewed to ensure that they accurately reflect the data. After further refining and defining themes to reflect the data, you can write up the analysis to explain how these patterns provide insights or relate to the research question.

Strengths

Limitations

Flexible approach that enables one to generate new insights and concepts
Accessible for novice researchers

Different ways to interpret data due to flexibility
As it focuses on common themes across interviews, issues that arise from one single data set may be overlooked

More Detailed Guides

Thematic vs Content Analysis

Their difference lies in their techniques and the level of detail.

Thematic analysis aims to uncover significant themes and their connections, offering a broad, overarching perspective.

In contrast, content analysis quantifies data by tallying the occurrence of specific terms or concepts, providing a more detailed examination.

SMU Libraries has a suite of tools for statistical and text analysis. For more information, refer to the libraries' research guide.

Related Resources

Research Cycle Overview

Bibliography

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