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Quantitative Data Analysis 1 (QDA1) Lecture Notes - GRADE 9,0 $11.24
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Quantitative Data Analysis 1 (QDA1) Lecture Notes - GRADE 9,0

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Quantitative Data Analysis 1 lecture notes from 13 lectures, including SPSS tips. The summary is 31 pages and includes notes from all lectures given in the course 6011P0218Y at UvA.

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  • September 22, 2020
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  • 2019/2020
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Quantitative Data Analysis 1

Lecture 1 03/02/20

Why statistics / quantitative data analysis?
 Research for science and decision making
 Data and software are accessible (e.g. SPSS)

Population and sample
 Population
o The complete group of interest
o All values within the whole group of interest
 Sample
o A subset of the population for which observations are gathered
o The observed values
 Random sampling
o Each member of the population has equal chance to enter the sample

Definition of variable types
 The statistical analysis you can do depends on the type of variable
 Quantitative (“scale variable”): measure a number (by nature)
o Continuous: interval of possible values
 (idealised view because in practice you always have to round the number)
 E.g. temperature in degrees Celsius, income in Euros
 Differences have meaning; hence also called “interval variable”
o Discrete: series of isolated possible values
 E.g. number of cars sold (0, 1, 2, 3, …), change in number of employees (…, -2, -1, 0,
1, 2 …)
o No clear division line between discrete and continuous
 Qualitative: measure a category
o Ordinal: ordered categories
 E.g. small/medium/large drink, job skill (very low, low, medium, high, very high)
o Nominal: unordered categories
 E.g. employed/unemployed, brand of a product
 Hierarchy in level of information
o Continuous – Discrete – Ordinal – Nominal
 Likert variable: used to measure judged
o E.g. agreement: 1=strongly disagree, 2=disagree, 3=neutral, 4=agree, 5=strongly agree
o A Likert variable is ordinal, not quantitative
o Still, in research it is often treated as quantitative. (Why?)
 This presupposes equal distances between successive categories
 This is justified if the categories are consistent with equal distances and a
quantitative scale with numbers is shown in the questionnaire.

Exploring your data
 For qualitative data (categorical) we use:
o Frequency table
o Bar chart
 To see and compare the frequencies and to see the order of categories if ordinal
o Pie chart
 To see the shares
o Mode (most frequent outcome)
o Median, only for ordinal data (middle outcome)
 For quantitative data we use:

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