M&S3
Week 1, Workshop 1
Assigning values to for example ‘job type’ can be done in variable view
- Double on the column value and the row that you want to assign the value
to
- Select the dots, and click on the plus sign to enter a new value (1; student,
2; lecturer)
Level of measurement
- Nominal: mutually exclusive but not order categories
o No ordering
o No calculations possible
o Eg: eye color, gender, ID number
- Ordinal: ordered, but differences between values are not
important
o Values simply express an order
o Eg: rankings, movie ratings
- Interval (scale): ordered, constant scale, but no natural zero
o Differences make sense, but ratio do not
o Eg: calendar dates, temperatures
- Ratio (scale): ordered, constant scale. Natural zero
o eg: zero on the kelvin scale is absolute zero, length time, counts,
age
o numbers/times/etc
Workshop 2
Data analysis methods:
Descriptive: summarize data;
- Mean mode median and range
- Variance, standard deviation
- Correlation between variables
- Count, max and min
Correlations: Compute the correlation values between different variables
(strong positive relation)
Crosstabs: Describe the relationship between two categorical variables
Compare means: summarize and compare differences in descriptive statistics,
across one or more factors or categorical variables. (eg; ice cream sales per
week)
To compute the mean or standard deviation go to analyze descriptive
statistics
- In the row of analyze all the data analysis methods can be found
,
, Week 2
Workshop 1
4 core types of visualization
- Relations between data points
o Two or more variables
- Comparison of data points
o One or more data sets
o Static: groups are usually visualised with a radar, bar chart or
clustered column chart
o Dynamic: Time-related data is usually visualized with a line or
column / vertical bar chart.
- Composition of data
o Data built up from different elements
- Distribution of data
o Data over time
Line chart = time
Bar chart = values need to be viewed intuitively
Column chart = data reflects the rank of values to focus on extreme values
Pie chart = relative proportion, changes over time
Area chart = energy consumption, part-to-whole relationship
histogram: shows frequency with the values of observations
Week 1, Workshop 1
Assigning values to for example ‘job type’ can be done in variable view
- Double on the column value and the row that you want to assign the value
to
- Select the dots, and click on the plus sign to enter a new value (1; student,
2; lecturer)
Level of measurement
- Nominal: mutually exclusive but not order categories
o No ordering
o No calculations possible
o Eg: eye color, gender, ID number
- Ordinal: ordered, but differences between values are not
important
o Values simply express an order
o Eg: rankings, movie ratings
- Interval (scale): ordered, constant scale, but no natural zero
o Differences make sense, but ratio do not
o Eg: calendar dates, temperatures
- Ratio (scale): ordered, constant scale. Natural zero
o eg: zero on the kelvin scale is absolute zero, length time, counts,
age
o numbers/times/etc
Workshop 2
Data analysis methods:
Descriptive: summarize data;
- Mean mode median and range
- Variance, standard deviation
- Correlation between variables
- Count, max and min
Correlations: Compute the correlation values between different variables
(strong positive relation)
Crosstabs: Describe the relationship between two categorical variables
Compare means: summarize and compare differences in descriptive statistics,
across one or more factors or categorical variables. (eg; ice cream sales per
week)
To compute the mean or standard deviation go to analyze descriptive
statistics
- In the row of analyze all the data analysis methods can be found
,
, Week 2
Workshop 1
4 core types of visualization
- Relations between data points
o Two or more variables
- Comparison of data points
o One or more data sets
o Static: groups are usually visualised with a radar, bar chart or
clustered column chart
o Dynamic: Time-related data is usually visualized with a line or
column / vertical bar chart.
- Composition of data
o Data built up from different elements
- Distribution of data
o Data over time
Line chart = time
Bar chart = values need to be viewed intuitively
Column chart = data reflects the rank of values to focus on extreme values
Pie chart = relative proportion, changes over time
Area chart = energy consumption, part-to-whole relationship
histogram: shows frequency with the values of observations
