Data Analysis Methods in Research: Complete Guide (2026)
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Data analysis is the process of examining, cleaning, transforming, and interpreting collected data to answer research questions and test hypotheses. The choice of analysis method is one of the most critical methodological decisions in any study — and it must be justified in your methodology chapter with reference to your research questions, data type, and philosophical position.
Types of Data Analysis: An Overview
| Category | Analysis Type | Data Type | Common Tools |
|---|---|---|---|
| Quantitative | Descriptive statistics | Numerical | SPSS, Excel, R |
| Quantitative | Inferential statistics | Numerical | SPSS, R, Stata |
| Quantitative | Regression analysis | Continuous/categorical | SPSS, R, Python |
| Quantitative | Structural Equation Modelling | Latent variables | AMOS, SmartPLS |
| Qualitative | Thematic analysis | Textual, interview data | NVivo, ATLAS.ti, manual |
| Qualitative | Content analysis | Documents, media | NVivo, MAXQDA, manual |
| Qualitative | Grounded theory coding | Interview/observation data | NVivo, ATLAS.ti |
| Qualitative | Discourse analysis | Language, text, speech | Manual, MAXQDA |
| Mixed | Integration (triangulation) | Both | Multiple tools |
Quantitative Data Analysis Methods
Descriptive Statistics
Descriptive statistics summarise the main features of a dataset without making inferences beyond the sample.
- Measures of central tendency: Mean, median, mode
- Measures of dispersion: Standard deviation, variance, range
- Frequency distributions and cross-tabulations
- Data visualisation: Histograms, bar charts, box plots
Inferential Statistics
Inferential statistics use sample data to make inferences about a population and test hypotheses. Key tests include:
| Statistical Test | Purpose | When to Use |
|---|---|---|
| Independent samples t-test | Compare means of two groups | Comparing two independent groups on a continuous DV |
| One-way ANOVA | Compare means of 3+ groups | Multiple group comparison on a continuous DV |
| Chi-square test | Test association between categorical variables | Frequency data, cross-tabulations |
| Pearson correlation | Measure strength of linear relationship | Two continuous variables |
| Spearman correlation | Non-parametric correlation | Ordinal data or non-normal distributions |
| Linear regression | Predict DV from one or more IVs | Continuous DV, prediction/explanation |
| Multiple regression | Predict DV from multiple IVs | Complex quantitative studies |
| Logistic regression | Predict binary DV | Yes/no outcomes |
| Structural Equation Modelling | Test complex models with latent variables | Large-scale survey research, path models |
Qualitative Data Analysis Methods
Thematic Analysis (TA)
Thematic analysis (Braun & Clarke, 2006/2021) is the most widely used qualitative analysis method. It identifies, organises, and reports patterns (themes) across a dataset. It is flexible and can be used within different philosophical frameworks.
The six phases: (1) Familiarise yourself with the data; (2) Generate initial codes; (3) Search for themes; (4) Review themes; (5) Define and name themes; (6) Write up the analysis.
Content Analysis
Content analysis systematically codes and categorises text, documents, or media to identify patterns, frequency of concepts, or communication trends. It can be qualitative (interpretive) or quantitative (counting occurrences).
Grounded Theory Coding
In grounded theory, data analysis uses three types of coding: open coding (labelling concepts), axial coding (linking categories), and selective coding (integrating categories around a core category). Analysis and data collection occur simultaneously through theoretical sampling.
Interpretative Phenomenological Analysis (IPA)
IPA explores how individuals make sense of their personal and social world. It involves close, ideographic analysis of transcripts to identify personal meaning. Used primarily in psychology and health research.
Discourse Analysis
Discourse analysis examines language use in social contexts — how language constructs meaning, power, and identity. Used in linguistics, sociology, and critical studies.
Mixed Methods Data Analysis
In mixed methods research, quantitative and qualitative data are analysed separately and then integrated. Integration can occur through:
- Triangulation — comparing findings from both strands to check convergence
- Explanation — using qualitative data to explain quantitative findings
- Exploration — using qualitative findings to inform quantitative instrument design
- Data transformation — converting qualitative codes into quantitative counts
Choosing Software for Qualitative Analysis
NVivo is the industry standard for qualitative analysis and is widely accepted by examiners. ATLAS.ti and MAXQDA are strong alternatives. However, software is a tool, not a methodology — you must understand the analytical approach (e.g., thematic analysis) independently of the software. Many examiners ask specifically about your analytical process, not just the software used.
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Frequently Asked Questions
Click a question to expand the answer.
The main data analysis methods in research are: Quantitative — descriptive statistics, inferential statistics (regression, ANOVA, t-test, chi-square), factor analysis, structural equation modelling (SEM); Qualitative — thematic analysis, content analysis, discourse analysis, grounded theory coding, narrative analysis, IPA (interpretative phenomenological analysis); Mixed methods — integration of both quantitative and qualitative analysis.
Thematic analysis (Braun & Clarke, 2006) is the most widely used method for qualitative data analysis. It involves systematically identifying, organising, and interpreting patterns (themes) across a dataset. The six phases are: (1) Familiarisation with data; (2) Generating initial codes; (3) Searching for themes; (4) Reviewing themes; (5) Defining and naming themes; (6) Producing the report.
Descriptive statistics summarise and describe the characteristics of a dataset — e.g., mean, median, mode, standard deviation, frequency distributions. Inferential statistics use sample data to make inferences or test hypotheses about a larger population — e.g., t-tests, ANOVA, correlation, regression. Descriptive statistics describe what the data looks like; inferential statistics test whether patterns are statistically significant.
Common software includes: SPSS (quantitative, widely used in social sciences and management); R and Python (advanced statistical and machine learning analysis); AMOS/Smart-PLS (structural equation modelling); ATLAS.ti, NVivo, and MAXQDA (qualitative analysis); Stata (economics and health sciences); Excel (basic descriptive analysis). The choice depends on your analysis methods and institutional availability.
Choose your analysis method based on: (1) Your research questions — are they exploratory or confirmatory? (2) Data type — categorical, continuous, nominal, textual? (3) Sample size — statistical tests require minimum sample sizes; (4) Research paradigm — positivist studies use statistical analysis; interpretivist studies use qualitative methods; (5) Disciplinary norms — what is standard in your field? Always align your analysis with your methodology chapter.