Statistics SPSS RUG 24-25
Watch clips before tutorials.
Go to tutorials, make the exercises before the tutorials.
Lecture 1 week 1:
Level of measurement:
,Quantitative data(Metric Numerical Data):
● Ratio data(Differences between measurements, true zero exists)
● Interval Data (Differences between measurements but no true zero)
Qualitative data(Categorical data):
● Ordinal data(ordered categories, rankings, order, or scaling))
● Nominal data(categories, no ordering or direction)
above is the highest
measurement level. So you can do more and more and more(look at green table).
Categories/labels(Nominal) = like the name of someone or a country, or male or
female so just description.
Ordered categories(Ordinal)= like the Sports competition being 1st or 2nd, or survey
ranking scales between 1 and 5
Interval data = difference between the different scale points is the same, like between 1
and 2 and 1 and 500. Like with temperature difference between 40 and 41 is the same
as 10 and 11 degrees.
,Ratio data: there is a natural zero. Age of 50 and 51 same as 20 and 21 but the age of
zero does have a meaning
Important consequences for what you can do statistically with the variable, also
for descriptive statistics.
From Nominal to Ratio: Data becomes more powerful, less restrictive
Properties of distributions: Characteristics of a variable
• Central tendency : the top
● Mode: most common in your database
● Median: is the middle, all numbers from lowest to highest, it's in the middle
● Mean: is the average
• Variability : the spread
, ● Range: max value - min value : the difference between the maximum and
minimum values in a dataset.
● standard deviation: a measure of how dispersed the data is in relation to the
mean
● Variance: measures variability from the average or mean.
• Skewness: ''is it more to the left(positive skew), right(negative skew) or more
symmetric(in the middle)?'' (mean, mode, median>look at example below)
Saving your analysis for next time: Paste to Syntax file
Frequency is the amount
Clip 1 week 1:
Statistics = The art and science of collecting, analyzing, presenting, and interpreting data.
Providing information to support decision making - Modern “synonym”: Data Science
Some terminology:
Database, data set(in SPSS, Excel,...) = total collection of all the data that is relevant to that
kind of topic.
Often as a data matrix:
Watch clips before tutorials.
Go to tutorials, make the exercises before the tutorials.
Lecture 1 week 1:
Level of measurement:
,Quantitative data(Metric Numerical Data):
● Ratio data(Differences between measurements, true zero exists)
● Interval Data (Differences between measurements but no true zero)
Qualitative data(Categorical data):
● Ordinal data(ordered categories, rankings, order, or scaling))
● Nominal data(categories, no ordering or direction)
above is the highest
measurement level. So you can do more and more and more(look at green table).
Categories/labels(Nominal) = like the name of someone or a country, or male or
female so just description.
Ordered categories(Ordinal)= like the Sports competition being 1st or 2nd, or survey
ranking scales between 1 and 5
Interval data = difference between the different scale points is the same, like between 1
and 2 and 1 and 500. Like with temperature difference between 40 and 41 is the same
as 10 and 11 degrees.
,Ratio data: there is a natural zero. Age of 50 and 51 same as 20 and 21 but the age of
zero does have a meaning
Important consequences for what you can do statistically with the variable, also
for descriptive statistics.
From Nominal to Ratio: Data becomes more powerful, less restrictive
Properties of distributions: Characteristics of a variable
• Central tendency : the top
● Mode: most common in your database
● Median: is the middle, all numbers from lowest to highest, it's in the middle
● Mean: is the average
• Variability : the spread
, ● Range: max value - min value : the difference between the maximum and
minimum values in a dataset.
● standard deviation: a measure of how dispersed the data is in relation to the
mean
● Variance: measures variability from the average or mean.
• Skewness: ''is it more to the left(positive skew), right(negative skew) or more
symmetric(in the middle)?'' (mean, mode, median>look at example below)
Saving your analysis for next time: Paste to Syntax file
Frequency is the amount
Clip 1 week 1:
Statistics = The art and science of collecting, analyzing, presenting, and interpreting data.
Providing information to support decision making - Modern “synonym”: Data Science
Some terminology:
Database, data set(in SPSS, Excel,...) = total collection of all the data that is relevant to that
kind of topic.
Often as a data matrix:
