Introduction
How to use this book
About the Author
I Introduction
1
What is Open Science?
1.1
The credibility of Science
1.2
Questionable scientific practices
1.3
The credibility crisis
1.4
Threats to the quality of the scientific process
1.5
Open Data
1.6
FAIR Data
1.7
Contributorship (CRediT)
1.8
References
2
Introduction to R
2.1
What is RStudio?
2.2
Let's get started!
2.2.1
Running a line of code
2.2.2
Adding comments on R scripts
2.3
Starting a session in RStudio
2.3.1
Setting the global options
2.3.2
First lines of code
2.3.3
Setting the working directory
2.4
R objects and functions
2.4.1
Functions
2.4.2
R objects
2.4.3
Applying functions to objects
2.4.4
Getting help
2.5
Packages
2.5.1
Loading R packages
2.6
Importing and exporting data
2.6.1
Loading data
2.6.2
Exporting data
2.7
References
3
Data Wrangling
3.1
Indexing and subsetting
3.1.1
Selecting columns (variables)
3.1.2
Selecting rows (observations)
3.1.3
Selecting rows and columns (conditional subsetting)
3.2
Adding new data to a data frame
3.3
Renaming, recoding, and sorting data
3.3.1
Renaming
3.3.2
Recoding
3.3.3
Sorting
3.4
The
tidyverse
3.4.1
The pipe (%>%)
3.4.2
The package
dplyr
3.4.3
The package
tidyr
3.5
Data wrangling with the
tidyverse
3.6
References
4
Descriptive Statistics and Data Visualization
II Modeling
5
Modeling in R
5.1
Some common statistical models
6
Linear Models I
7
Linear Models II
8
Linear Models III
9
Generalized Linear Models
10
Categorical Data Analysis
10.1
Categorical data
10.1.1
Binomial and multinomial data
10.1.2
Ordinal data
10.2
Dealing with categorical variables in R
10.3
Research questions with categorical data
10.4
References
11
Linear Mixed Models
III Psychometrics
12
Scale Development
12.1
Reflective and formative indicators
12.2
Classification of scales of measurement
12.2.1
Nominal scale
12.2.2
Ordinal scale
12.2.3
Interval scale
12.2.4
Ratio scale
12.3
Test theory
12.3.1
What is a test?
12.3.2
Classification of psychological tests
12.3.3
Measurement error
12.4
Scale development
12.4.1
Purpose of the test
12.4.2
Administration restrictions
12.4.3
Defining the domain
12.4.4
Test specification (blueprint)
12.4.5
Draft the initial pool of items, review them, and pilot them
12.4.6
Psychometric principles: Reliability, validity, comparability, and fairness
12.4.7
Field test and the development of guidelines for administration, scoring, and interpretation of test scores
12.5
References
13
Exploratory Factor Analysis
13.1
Data reduction techniques
13.1.1
Principal Components Analysis (PCA)
13.1.2
Factor Analysis (FA)
13.2
Exploratory Factor Analysis (EFA)
13.2.1
The basic EFA model
13.2.2
Steps to conduct an Exploratory Factor Analysis (EFA)
13.3
Initial considerations
13.3.1
Tidying the data set
13.3.2
Generating the correlation matrix
13.3.3
Exploring the data using descriptive statistics and data visualizations
13.4
Factor extraction
13.5
Factor rotation and interpretation of EFA
13.6
References
14
Reliability
14.1
Approaches to reliability
14.2
Reliability methods requiring several administrations of a test
14.2.1
Test-retest
14.2.2
Parallel forms
14.3
Inter-rater reliability
14.3.1
Cohen's Kappa
14.3.2
Cohen's Kappa in R
14.4
Internal consistency
14.4.1
Split half tests
14.4.2
Item covariance tests
14.5
Internal consistency in R
14.5.1
Data wrangling
14.6
Cronbach's alpha (
\(\alpha\)
)
14.7
Other reliability coefficients
14.8
References
15
Introduction to Item Response Theory
15.1
Item Response Theory (IRT)
15.2
Item Characteristic Curve (ICC)
15.2.1
Example: Depression scores
15.3
IRT Models
15.4
Parameters
15.4.1
Item difficulty (
\(b_{j}\)
)
15.4.2
Item discrimination (
\(a_{j}\)
)
15.4.3
Guessing (
\(c_{j}\)
)
15.5
The Information Function
15.5.1
Item Information Function
15.5.2
Test Information Function
15.6
References
Appendices
A
Software
A.1
Installation of R and RStudio
A.2
Installation of R packages
A.3
Packages used in this book
B
Stylistic Conventions
C
Universal Design for Learning
Published with bookdown
Learning Data Science with R
C
Universal Design for Learning
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