Section outline
-
The analysis of research in any project involve summarising the mass of data that has been collected and the presenting the results in a way that communicates the most important findings or features
-
Learning outcomes
1. Describing data (types of data, data visualization, descriptive statistics)
2. Statistical inference (probability, probability distributions, sampling theory, hypothesis testing, confidence intervals, pitfalls of p-values)
3. Specific statistical tests (ttest, ANOVA, linear correlation, non-parametric tests, relative risks, Chi-square test, exact tests, linear regression, logistic regression, survival analysis; how to choose the right statistical test)Stanford University
-
Content analysis involves coding and classifying data, also referred to as categorising and indexing and the aim of context analysis is to make sense of the data collected and to highlight the important messages, features or findings.
-
STEP 1, reading the transcripts
1.1. Browse through all transcripts, as a whole.
1.2. Make notes about your impressions.
1.3. Read the transcripts again, one by one.
1.4. Read very carefully, line by line. -
Computer Assisted/Aided Qualitative Data Analysis Software (CAQDAS) offers tools that assist with qualitative research such as transcription analysis, coding and text interpretation, recursive abstraction, content analysis, discourse analysis, grounded theory methodology, etc.
https://en.wikipedia.org/wiki/Computer-assisted_qualitative_data_analysis_software
-
Data interpretation may be the most important key in proving or disproving your hypothesis. It is important to select the proper statistical tool to make useful interpretation of your data. If you pick an improper data analysis method, your results may be suspect and lack credibility.