A Minimal Book Example
1
Getting Started
1.1
Download the Softwares
1.1.1
Step 1: Download
R
1.1.2
Step 2: Download
RStudio
1.2
Save and Run Your Code
1.2.1
Type and Save your code in the source editor
1.2.2
Creating New Folders
1.2.3
Type, Run, and Save a line of code
1.2.4
Only Save the Correct Code
1.3
How to Open Data in R
1.3.1
Set Working Directory
1.3.2
Read Excel Data
1.3.3
Read SPSS Data
1.4
Some Basic Concepts
1.4.1
What is a ‘case?’
1.4.2
Data and Statistics
1.4.3
Estimation
1.4.4
Variable
1.4.5
Categorical Variables and Quantitative Variables
1.5
Exercises
1.5.1
Identify main variables in research summaries
1.5.2
Practice Using R
2
How to Visualize a Single Variable
2.1
Get to know the dataset
2.2
Visualizing a Categorical Variable
2.2.1
Bar plot
2.2.2
Pie Chart
2.2.3
Bar or Pie?
2.2.4
Your Turn:
2.3
Visualizing a Quantitative Variable
2.3.1
Histogram
2.3.2
Boxplot
2.3.3
Comparing a Histogram with a Bar Plot
2.3.4
Your turn
2.4
Something Else about Categorical Variables–Risk and Change of Risk
2.4.1
Example 1
2.4.2
Example 2
2.4.3
Example 3
2.5
Exercise
3
Describing a Quantitative Variable
3.1
Plotting the Distribution
3.2
Measures of Central Tendency
3.3
Measures of Variability/Dispersion
3.4
Standard Deviation
3.4.1
How to Calculate Standard Deviation
3.4.2
What does the standard deviation mean?
3.5
Exercises
4
The Normal Distribution
4.1
Why is the Normal Distribution so important?
4.2
What is a normal distribution?
4.3
The z-score: How the normal distribution helps us making judgement
4.4
Exercises
5
Correlation
5.1
Plotting the relationship between 2 Variables
5.2
Correlation
5.3
What kind of data are suitable for correlation?
5.4
Independent and Dependent Variables
5.5
How to create a scatter plot
5.6
How to Read a scatterplot
5.7
The Correlation Coefficient, aka Pearson’s r
5.8
What the correlation coefficient does and does not mean
5.8.1
r is not the slope
5.8.2
r only represents linear relationship
5.8.3
r could be unduely influenced by outliers
5.9
Exercises
5.9.1
The lowest magnitude of correlation shown below is:
5.9.2
Most of the examinees who score below the mean on Test 1 also scored below average in Test 2; the correlation between the two tests appears to be:
5.9.3
If the coefficient of correlation between X and Y is found to be -0.98, which of the following would be indicated?
5.9.4
Given r (correlation coefficient)= 0.50 between X and Y, it follows that:
5.9.5
Interpret the meaning of each of the following correlation coefficients (you can do so after the examples, or feel free to use your own language if you could try to stay close to the interpretations provided here):
5.9.6
Play with the dataset called “wvs” (world value survey).
6
Causation
6.1
Experimental Studies
6.2
Correlational/Observational Studies
6.3
Coming up with alternative explanations
6.4
Sample
6.5
Exercises
6.5.1
Critique the following research studies from the perspectives of design and sample.
6.5.2
Provide alternative explanations for the following correlations
7
Hypothesis Tests
7.1
Independent and Dependent Variables–Recap
7.2
Research Hypothesis and Null Hypothesis
7.3
Hypothesis Test–Step by Step
7.3.1
What do we do when we “analyze the data?”
7.3.2
Conclusion
7.4
One-Sample t Test
7.5
Type I and Type II Errors
7.5.1
Type I Error
7.5.2
Type II Error
7.5.3
The relationship between type I and II errors
7.6
Exercises
7.6.1
Identify the independent and dependent variables
7.6.2
We’d like to research the idea that praising children for their effort makes them more resilient. Please lay out the steps for the hypothesis test.
7.6.3
Which ones of the following are appropriate null hypotheses?
7.6.4
True or False
7.6.5
Decimals and percentages
7.6.6
The scientific notation
7.6.7
Investigators wish to study the question, “do blondes have more fun?”
7.6.8
Type I and II Errors
7.6.9
One-sample t tests
8
Analysis of Variance
8.1
Why is it called Analysis of
Variance
?
8.1.1
Comparing Tarons and Americans.
8.1.2
Comparing American crocodiles and American alligators.
8.2
Doing Analysis of Variance
8.3
Exercises
8.3.1
Run an ANOVA comparing reading scores from spring of first grade between public and private school students.
8.3.2
Run an ANOVA comparing reading scores from fall of kindergarten between public and private school students.
8.3.3
Correlation and causation
9
Chi-Square Test
9.1
How does the chi-square test work?
9.2
Carrying out a chi-square test
9.3
Exercises
9.3.1
Choose the appropriate kind of hypothesis test for the following research questions
9.3.2
Carry out the appropriate hypothesis tests to answer the following questions
10
Paired T Test
10.1
When do we use paired t test?
10.2
How to carry out paired t test?
10.3
Difference and Relationship
10.4
Exercises
10.4.1
Choose the appropriate kind of hypothesis test for the following research questions
10.4.2
Carry out a paired-sample t test for each of the following research questions using the dataset “ecls.sav.”
References
Published with bookdown
Making Sense of Data with R
References