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Data Processing &
Data Visualization
Dr Renata Brandao
PDRA Digital Mediations
Language Acts and Worldmaking
Powerful rhetorical instruments intheconstruction
ofdiscourses (Battersby, 2003; Gigerenzer, 2002;
Kahneman, Slovic &Tversky, 1982).
1.Introduction toSPSS
2.Data Entry andProcessing
3.Hypothesis Testing
Later:
4.Data Visualisation
This session:
DataCycle:
Data
Collection
Data Analysis
Insight
Data
Visualization
Data
Variables
Numerical
Continuous Discrete
Categorical
Ordinal Nominal
DataProcessingSoftware:
SPSS
SAS
STATA
STRATA
MATLAB
R
EULER
MINITAB
STATISTICA
SPSSSTATISTICS
SPSSMODELERAutomated
modeling;Geospatialanalytics;Open
sourcetechnologies;Textanalytics.
SPSSTEXTANALYTICSFORSURVEYS
SPSS
As seen in EXCEL
As seen in SPSS
• Define variables first
• Can add or change variables
at any stage
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:
Stateyournull hypothesis(H0)
(statementyouarelookingforevidenceto
disprove)
Stateyourstudy(alternative) hypothesis(H1
orHA)whichisusuallytheresearchquestion
Hypothesistestinghelpsdecidewhichof
thesestatementsismorelikelytobetrue
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
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!
Visualization toolspose
manychallengesbut
alsoopportunities.
• - Juxtapose
• - Hierarchy
• - Proximity
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)
DataVisualization Software:
Infogram
Piktochart
Charts.js, D3.js,
HighChart.js and
Graphaël
Tableau
Plotly
HighCharts
Periscope
IBM Watson Analytics
Beforestarting,tryout:
https://datavizcatalogue.com
Also worth checking:
https://informationisbeautiful.net/visualizations/w
hat-makes-a-good-data-visualization/
Thank you!
Questions?
renata.brandao@kcl.ac.uk
EXTRA

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Data Processing and Data Visualization

Editor's Notes

  1. Data Collection: What data will you need to consider your interest? Data Analysis: Data to Information – raw data rarely answer the questions, statistical methods are needed for that! Insight and DataViz: Summarise the Information – present it within context, not just as statistical results
  2. Categories can be assigned as codes
  3. Values can be added to codes in Variables View Values can be seen when Labels icon is on
  4. Delete?