LECTURE 1 FROM RESEARCH
PROBLEM TO ANALYSIS
GENERAL INFORMATION COURSE
Course goal
Bring prior information on research methods together, and expand.
Prepare you for:
o Master thesis
o Future career
o Not just for end paper
Course components
Lectures, tutorials, computer labs
o Linked, but not completely overlapping.
Brightspace
o All info
o Week-by-week
o Preparations before sessions
Coordinators
o Sarah Plukaard:
o Jasper Maas:
To pass the course…
Participate in all tutorials and computer labs
Review assignment (no grade)
End paper (grade 5.5 of higher)
STATISTICS TECHNIQUES
Statistical techniques from the bachelor program
Z-test & t-test
Correlation & regression
ANOVA
Non-parametric tests
THIS IS ALL THE TECHNIQUES FOR YOUR END PAPER!!!!!
,What’s new in PEO?
FROM RESEARCH PROBLEM TO ANALYSIS
Multivariate analysis: the basics
Bivariate analysis: the relationship between 2 variables
o X (horizontal) Y (vertical)
o Purpose
To identify correlations or associations.
To test hypotheses about how variables relate.
o Common methods
For 2 numeric variables: correlations (Pearsons,
Spearman), scatterplots, regression.
For 1 numeric & 1 categorical variable: t-test,
ANOVA
For 2 categorical variables: chi-square test, cross-
tabulation
Multivariate analysis: relations between multiple variables at a
time.
o Variables
X1 & X2…? Y
X Y1 & Y2…?
Both
o Purpose
To see how several variables together influence an
outcome.
To control for confounding variables variables that
might distort the main relationship.
To identify patterns or groupings in complex data.
o Types/methods
, Multiple regression: examines how several
independent variables affect a single dependent
variable.
MANOVA (multivariate ANOVA): compares groups on
multiple dependent variables simultaneously.
Factor analysis : reduces many variables into fewer
underlying factors.
Cluster analysis: groups observations based on
multiple variables.
Canonical correlation: studies the relationship
between two sets of multiple variables.
Complexity of research questions
Univariate descriptive: one variable at a time and describes its
main features.
o Purpose
To summarize & describe a single variable
To understand its distribution, central tendency and
spread.
o Common descriptive measures
For numeric (continuous) variables
Mean
Median
Mode
Range, variance, standard deviation
For categorical variables
Frequency counts
Percentages/proportions
Bivariate: a descriptive way to summarize how two variables relate.
o Purpose
To summarize how two variables vary together.
To detect patterns, trends or associations.
o Common descriptive tools
Cross-tabulation
Scatterplots
Correlation coefficients
Grouped summaries
o Symmetric: the relationship between 2 variables is mutual
no distinction between ‘cause’ (predictor) and ‘effect’
(outcome).
No direction implied.
o Asymmetric, non-causal relationship: one variable is
considered the independent or predictor variable and the other
, is the dependent variable, but this relationship does not imply
causation. It just describes how 1 variable varies with another.
Directional but not causal
o Asymmetric, causal relationship: one variable causes of
influences the other. This is the typical focus in experimental
research or causal modeling.
Directional and causal.
Causali
Type Direction Example
ty
Symmetric None No Height vs. weight
Asymmetric non- One → Study hours → exam scores
No
causal other (descriptive)
One →
Asymmetric causal Yes Fertilizer → plant growth
other
Multivariate: summarize and describe more than 2 variables at the
same time. Looks at patterns, relationships and distributions across
multiple variables simultaneously.
o Purpose: to understand complex interactions, patterns or
structures among several variables at once.
o Examples of multivariate descriptives
Means and standard deviations for multiple variables
together.
Correlations matrices
Covariance matrices
Cross-tabulation
Multivariate plots
o When uses: often before performing advanced analyses like
factor analysis, multiple regression or cluster analysis to
understand the structure of the date.
Matching problem with technique
PROBLEM TO ANALYSIS
GENERAL INFORMATION COURSE
Course goal
Bring prior information on research methods together, and expand.
Prepare you for:
o Master thesis
o Future career
o Not just for end paper
Course components
Lectures, tutorials, computer labs
o Linked, but not completely overlapping.
Brightspace
o All info
o Week-by-week
o Preparations before sessions
Coordinators
o Sarah Plukaard:
o Jasper Maas:
To pass the course…
Participate in all tutorials and computer labs
Review assignment (no grade)
End paper (grade 5.5 of higher)
STATISTICS TECHNIQUES
Statistical techniques from the bachelor program
Z-test & t-test
Correlation & regression
ANOVA
Non-parametric tests
THIS IS ALL THE TECHNIQUES FOR YOUR END PAPER!!!!!
,What’s new in PEO?
FROM RESEARCH PROBLEM TO ANALYSIS
Multivariate analysis: the basics
Bivariate analysis: the relationship between 2 variables
o X (horizontal) Y (vertical)
o Purpose
To identify correlations or associations.
To test hypotheses about how variables relate.
o Common methods
For 2 numeric variables: correlations (Pearsons,
Spearman), scatterplots, regression.
For 1 numeric & 1 categorical variable: t-test,
ANOVA
For 2 categorical variables: chi-square test, cross-
tabulation
Multivariate analysis: relations between multiple variables at a
time.
o Variables
X1 & X2…? Y
X Y1 & Y2…?
Both
o Purpose
To see how several variables together influence an
outcome.
To control for confounding variables variables that
might distort the main relationship.
To identify patterns or groupings in complex data.
o Types/methods
, Multiple regression: examines how several
independent variables affect a single dependent
variable.
MANOVA (multivariate ANOVA): compares groups on
multiple dependent variables simultaneously.
Factor analysis : reduces many variables into fewer
underlying factors.
Cluster analysis: groups observations based on
multiple variables.
Canonical correlation: studies the relationship
between two sets of multiple variables.
Complexity of research questions
Univariate descriptive: one variable at a time and describes its
main features.
o Purpose
To summarize & describe a single variable
To understand its distribution, central tendency and
spread.
o Common descriptive measures
For numeric (continuous) variables
Mean
Median
Mode
Range, variance, standard deviation
For categorical variables
Frequency counts
Percentages/proportions
Bivariate: a descriptive way to summarize how two variables relate.
o Purpose
To summarize how two variables vary together.
To detect patterns, trends or associations.
o Common descriptive tools
Cross-tabulation
Scatterplots
Correlation coefficients
Grouped summaries
o Symmetric: the relationship between 2 variables is mutual
no distinction between ‘cause’ (predictor) and ‘effect’
(outcome).
No direction implied.
o Asymmetric, non-causal relationship: one variable is
considered the independent or predictor variable and the other
, is the dependent variable, but this relationship does not imply
causation. It just describes how 1 variable varies with another.
Directional but not causal
o Asymmetric, causal relationship: one variable causes of
influences the other. This is the typical focus in experimental
research or causal modeling.
Directional and causal.
Causali
Type Direction Example
ty
Symmetric None No Height vs. weight
Asymmetric non- One → Study hours → exam scores
No
causal other (descriptive)
One →
Asymmetric causal Yes Fertilizer → plant growth
other
Multivariate: summarize and describe more than 2 variables at the
same time. Looks at patterns, relationships and distributions across
multiple variables simultaneously.
o Purpose: to understand complex interactions, patterns or
structures among several variables at once.
o Examples of multivariate descriptives
Means and standard deviations for multiple variables
together.
Correlations matrices
Covariance matrices
Cross-tabulation
Multivariate plots
o When uses: often before performing advanced analyses like
factor analysis, multiple regression or cluster analysis to
understand the structure of the date.
Matching problem with technique
