This document provides an overview of data processing, analysis, and visualization. It discusses using software like SPSS, SAS, and R for processing numerical, categorical, and other variable data. The document demonstrates how to define variables, perform descriptive and inferential statistics to test hypotheses and draw conclusions. It also discusses challenges and opportunities of data visualization and recommends tools like Tableau, Plotly, and IBM Watson Analytics for creating visualizations.

1. Data Processing &
Data Visualization
Dr Renata Brandao
PDRA Digital Mediations
Language Acts and Worldmaking

2. Powerful rhetorical instruments intheconstruction
ofdiscourses (Battersby, 2003; Gigerenzer, 2002;
Kahneman, Slovic &Tversky, 1982).

3. 1.Introduction toSPSS
2.Data Entry andProcessing
3.Hypothesis Testing
Later:
4.Data Visualisation
This session:

4. DataCycle:
Data
Collection
Data Analysis
Insight
Data
Visualization

5. Data
Variables
Numerical
Continuous Discrete
Categorical
Ordinal Nominal

6. DataProcessingSoftware:
SPSS
SAS
STATA
STRATA
MATLAB
R
EULER
MINITAB
STATISTICA

7. SPSSSTATISTICS
SPSSMODELERAutomated
modeling;Geospatialanalytics;Open
sourcetechnologies;Textanalytics.
SPSSTEXTANALYTICSFORSURVEYS
SPSS

8. As seen in EXCEL

9. As seen in SPSS

10. • Define variables first
• Can add or change variables
at any stage

14. 1.Pointestimation,
2.Intervalestimation
3.Hypothesistesting.
 Descriptive statistics summarise and describe the
sample data we have collected
 Inferential statistics obtained from sample data and
used to infer something about the wider population
Main Types of Inferences:

15. Stateyournull hypothesis(H0)
(statementyouarelookingforevidenceto
disprove)
Stateyourstudy(alternative) hypothesis(H1
orHA)whichisusuallytheresearchquestion
Hypothesistestinghelpsdecidewhichof
thesestatementsismorelikelytobetrue

16. Make a decision and conclude
Compare p-value with 0.05
Set null and alternative hypothesis
Calculate a test statistic and its p-value
Define study question and choose test

17. Exercise 1:
What would the null and alternative hypotheses be for this research
question?
 Did class affect survival on board the Titanic?
Exercise 2:
 Calculate the % of people dying within each class
 Who was most likely to die?
 What % of people in each class would have died if the null was
true?
Exercise 3: Interpret the Output!

19. Visualization toolspose
manychallengesbut
alsoopportunities.
• - Juxtapose
• - Hierarchy
• - Proximity

20. DataViz
Rhetoric
“The challenge is to break the literalism of
representational strategies and engage with innovations
in interpretive and inferential modes that augment
human cognition.” (Drucker, 2014, p. 71)

21. DataVisualization Software:
Infogram
Piktochart
Charts.js, D3.js,
HighChart.js and
Graphaël
Tableau
Plotly
HighCharts
Periscope
IBM Watson Analytics

22. Beforestarting,tryout:
https://datavizcatalogue.com
Also worth checking:
https://informationisbeautiful.net/visualizations/w
hat-makes-a-good-data-visualization/

28. Thank you!
Questions?
renata.brandao@kcl.ac.uk

29. EXTRA