summary R guide research methodology test 2

Research methodology – Test 2

Based on all learning goals

ChatGPT was not used for the content, but was used for language and grammar.

A detailed summary, including supporting images from the lesson PowerPoints


Unit 13, 14, 24, 12, 15, 16, 17, 18, 19, 23, 20, 22




1

,Contents
Research methodology – Test 2........................................................................................................... 1
Unit 13 ................................................................................................................................................. 3
Unit 24 ................................................................................................................................................. 5
Practice examples from the lessons ................................................................................................ 9
Unit 12 ............................................................................................................................................... 14
Unit 15 ............................................................................................................................................... 18
Unit 14 ............................................................................................................................................... 21
Practice examples from the lessons .............................................................................................. 24
Unit 16 ............................................................................................................................................... 34
Unit 17 ............................................................................................................................................... 37
Unit 18 ............................................................................................................................................... 38
Unit 19 ............................................................................................................................................... 49
Unit 23 ............................................................................................................................................... 54
Unit 20 ............................................................................................................................................... 62
Unit 22 ............................................................................................................................................... 64
Summary R guide............................................................................................................................... 66




2

,Unit 13
1. Univariate Graphs and Tables (Univariate Data):
• Univariate means "one variable." This means you are looking at only one
characteristic of your data.
• Univariate graphs and tables provide information about the distribution, frequency,
and other properties of a single variable.
Examples of univariate graphs:
• Histogram: Shows the distribution of a continuous variable (e.g., heights or ages).
• Bar chart: Shows the frequency or count for each category of a categorical variable
(e.g., number of people per country).
• Boxplot: Shows the spread of a continuous variable, including the median and
quartiles.
When do you use univariate graphs?
• Use univariate graphs and tables when you only want to examine how a single
variable behaves or is distributed.


2. Bivariate Graphs and Tables (Bivariate Data):
• Bivariate means "two variables." This means you are looking at the relationship
between two variables and how they are connected.
• Bivariate graphs and tables show the relationship between two variables, such as
correlation or dependency.
Examples of bivariate graphs:
• Scatterplot: Shows the relationship between two continuous variables (e.g., height vs.
weight).
• Stacked bar charts: Show the distribution of one categorical variable against another
(e.g., product preference by gender).
• Line chart: Can show the relationship between two variables over time (e.g.,
temperature by month).
When do you use bivariate graphs?
• Use bivariate graphs and tables when you want to investigate the relationship
between two variables — for example, whether there is a connection between a
person’s height and weight.




3

, Create a scatterplot (using statistical software and by hand) with the independent variable
on the X-axis and the dependent variable on the Y-axis; example from R guide:
• Create a scatterplot in R with the variables Work_time and Income_Work
• students %>%
• ggplot(aes(x = Work_time, y = Income_Work)) +
• geom_point()




•



1. Bivariate Analysis:
• What it is: The examination of the relationship between two variables.
• How to interpret: Use bivariate analysis to understand whether there is a connection
between the two variables. For example, you can investigate whether there's a
relationship between the number of study hours and the grades a student achieves.


2. Contingency Table:
• What it is: A table that displays the frequencies or counts of combinations of two
categorical variables.
• How to interpret: A contingency table helps you identify patterns or relationships
between two categorical variables. For example, how often certain preferences
(yes/no) occur across different age groups.


3. Scatterplot:
• What it is: A graph that shows the relationship between two continuous variables
using points on an XY-axis.
• How to interpret: If the points follow a clear pattern, such as a straight line, there is
likely a linear relationship between the variables. If the points are scattered without a
clear pattern, the relationship is weaker or non-linear.


4. Regression Line:
• What it is: A line that best fits the data in a scatterplot. It shows the expected value of
the dependent variable based on the independent variable.


4