What is Quantitative Data? + downsides (4)
Structured Data
- people cannot be measured in numbers
- isn’t as transparent
- data reduction
- lack of normal data distribution
- Ordering & Collecting Data in Quantitative Research
Data collected is noted down in the Code Book
- providing an overview of all the variables
is plotted in the Data Matrix (units: rows, variables: columns)
- Data Inspection in Quantitative Data
Data inspection is key to get rid of outlier data.
Thus, data is reduced to have a representative collection of the sample studied.
- trace back & recode
- Data Analysis on Quantitative Data
There are 2 types
1. Descriptive: focuses on the data characteristics and the correlation between these.
2. Inferential: focuses on the statistical description of the data
3.1. Descriptive Analysis in Quantitative Data
(methods 3)
- Cross tabulation: juxtaposition of 2 variables
- Correlation: positive or negative correlation of the variables (linear relation)
- T-Test: statistical significance of the variables
3.2. Inferential Analysis in Quantitative Data
(methods 3)
- Regression Analysis: linear relation indicating a significant effect and the magnitude of the variables.
- Factor Analysis: correlation of variables based on 1 factor
- Variance Analysis: systematic differences between 2 groups (pre/post test)
- Reporting Results in Quantitative Data
- Conclusion
- Type of presentation
If the data analysis in Descriptive, the results will be presented in tables.
If the data analysis is Inferential, the results will be presented by rejecting/confirming hypotheses.
- What was measured?
- How is it interpreted?
Validity & Reliability of Quantitative Data (4)
- Sample Composition & Representativeness (exclude overrepresentation).
- Analysis suffering from statistical artefacts (exclude outliers to meet normal distribution).
- Theoretical explanation based on statistical analysis.
- Unexplained variance leads to further research.
