1  Introduction to Educational Research and R

Research in any field involves questions and data used to answer them. Cartoon by Manfred Steger.

1.1 Dramatis Personae

Curiosa: The central character of our story. She is a curious learner, and is full of all types of questions about all kind of things. She is our young researcher who has just set the journey towards doing educational research.

Simplicio: A peer of Curiosa (with apologies to Galileo) who is not curious and is likely to just get things done without understanding.

Faustus: A shady character who tries to derail the curious quest of Curiosa.

Pedagorgon: A classical pedagogue, doesn’t want any experiments in the classroom.

Also featuring at times in cameo roles:

Author: The opinions of the author. With this voice the author (mis)directs the thoughts of the reader and provokes them.

Reader: The opinions of the imagined reader. Though can also be called the B-team of the author.

2 What is research?

Research in any field provides its practitioners with a deeper understanding of fundamental aspects of the field. Likewise research in education intends to provides educators insights into various aspects of educational practice and theory. For example, a research study might investigate the effectiveness of a new teaching method on student scores, or how different teaching strategies affect the conceptual understanding in science. Learning about educational research requires both an understanding of research design and basic statistical concepts. In this book, we will touch upon the required ideas of research design, and dig deeper into the statistical ideas which substantiate them.

Any research is driven by questions. (CC by SA https://soco-st.com/11103)

Any research is driven by questions, and framing good questions is equally important as the answers. Many scientific advances happened because certain questions were asked and answered. Framing the research question is the first step towards research. Consider how the question ‘What is the structure of DNA?’ led to the discovery of the double helix, or ‘How do vaccines work?’ revolutionized public health. These questions may arise from some gap in our understanding or knowledge or some curiosity. For example, a researcher might ask does using mother tongue as the medium of instruction improve comprehension in early grades? We then seek to answer these questions by collecting data, and then analysing the data. Continuing the above example, one might collect assessment scores, interview the teachers, or carry out classroom observations from classrooms using different languages.

In educational research, we have two major types of data, namely, qualitative and quantitative. The type of analysis we perform depends on the nature of the data. In this book, we mostly deal with quantitative data. The data analysis then informs us about the questions we had raised, we may find correlations between variables, or causal relations between them, we may even form a theory. For example, analysing student scores in our mother tongue study we might find a correlation between use mother tongue in primary grades and higher scores. The answer to our question is shared with peers in the form of publications in journals and presentations in conferences where it is put under scanner. This answer/theory may raise further questions and we are back to the first step. Thus research is a cyclic process.

The research cycle. At each step we are making certain assumptions that we may not be fully aware of. We start with questions and end up with more questions!

In this view the, sometimes called as the research cycle, the research process is thus cyclic and involves continuous engagement between questions, assumptions, theory and data. While often seen as a practical manifestation of the scientific method, the research cycle applies more broadly to various forms of inquiry, not just experimental science. There are several steps, involved in the research cycle as shown in the broadly oversimplified figure above. Though not often stated explicitly, such a view also arises from and assumes a specific philosophical views about the world. We briefly touch upon these views next.

In this work, we are assuming the position of realism, which holds a view that there is a reality independent of our perceptions. The understanding of this reality is always mediated by theories, perspectives and contexts in which we are working. Thus the research cycle is a cycle of approximation, aspiring for verisimilitude though never quite reaching it. Thus all knowledge obtained through the research cycle is tentative knowledge that we can best assert to from the data that we have. History of science is full of such examples where what was assumed to be true (by scientific consensus of the time) was later shown to be wrong. Classic examples include the shift from the geocentric to the heliocentric model of the solar system, or the earlier belief in phlogiston theory before the understanding of oxidation. Thus, in realism the emphasis is on the the idea that knowledge evolves through successive cycles of observation, interpretation, and refinement.

In educational context, the cyclic view of research closely aligns with the philosophy of pragmatism as developed by John Dewey and Charles Sanders Peirce. Pragmatism treats inquiry as a problem-solving process, where ideas are tested and revised in practice. This inquiry is an iterative and reflective process informed by experience. The knowledge inquiry generates is not static, it continuously evolves with new understandings and changing contexts. For example, early educational theories often focused solely observable outcomes discounting any non-observable mental structures, but as our understanding of cognitive science evolved, more nuanced theories positing mental structures as being key to learning emerged.

Also inherent in both the positions above is the assumption that by studying the real (observing/experimenting) world we can understand it. This forms the basis of all our research. We will return to these issues several times later with context of interpreting our analysis of data, methodologies and inferring actionable outcomes.

Of course, we are over simplifying the philosophical accounts here, going any further will take us to deeper philosophical waters than we intend to go at this stage. But this philosophical background is often ignored because it is so “obvious” as this belief is rarely questioned. But being aware of these issues helps us engage better with research methods and their implications. A reminder that all research inquiry rests on particular ways of seeing and understanding the world and all our knowledge about the natural world is tentative and subject to change.

3 What type of questions can we ask?

3.1 Causality

One of the most important ideas in scientific research is trying to understand how two things are related. Research in general, and statistical methods in particular are used to establish that changes in one thing (A) are responsible for changes in another thing (B). Such a relationship is called as a causal relationship, and we say A causes B. For example, we know that smoking causes lung cancer.

Establishing a causal relationship is one of the most important methological tools of scientific research. And causal relationships help us to understand nature

When we are certain, that thing A causes thing B, we say that th

Research Questions Can Be Classified into These Types

1. Confirmatory Questions

Confirmatory questions are designed to test specific hypotheses or predictions. They aim to validate or refute pre-existing theories or assumptions using empirical data. These questions are typically used in quantitative research and rely on statistical methods to determine relationships, effects, or differences.

Use Cases

  • Testing the effectiveness of an educational intervention (e.g., “Does a new teaching method improve student performance?”)
  • Validating a model or theory in psychology or sociology.
  • Determining causal relationships between variables in experimental studies.

2. Exploratory Questions

Exploratory questions aim to investigate unknown or poorly understood phenomena. They are open-ended and flexible, focusing on generating new insights, ideas, or hypotheses rather than definitive answers. These questions are common in qualitative research and often involve interviews, focus groups, or ethnographic studies.

Use Cases

  • Understanding how students perceive online learning (e.g., “What are students’ experiences with virtual classrooms?”)
  • Exploring attitudes toward a new policy or program.
  • Identifying emerging trends or patterns in educational practices.

3. Inferential Questions

Inferential questions seek to draw conclusions about a population based on sample data. These questions often involve statistical inference techniques, such as regression analysis, correlation studies, or hypothesis testing. They aim to uncover relationships, predict outcomes, or generalize findings.

Use Cases

  • Predicting student drop-out rates based on socio-economic factors (e.g., “How does parental income influence the likelihood of high school graduation?”)
  • Examining the relationship between study habits and academic performance.
  • Assessing the impact of teacher qualifications on student achievement.

4. Descriptive Questions

Descriptive questions focus on summarizing characteristics, behaviours, or phenomena without making predictions or testing hypotheses. They answer “what,” “how,” “when,” and “where” questions using quantitative data (e.g., surveys) or qualitative observations.

Use Cases

  • Measuring attendance rates across different grade levels (e.g., “How often do students attend school?”)
  • Identifying demographic characteristics of a study population.
  • Tracking trends in technology usage among students (e.g., “What percentage of students use tablets for learning?”).

A cartoon with a question mark.

What is question that drives your research? Cartoon by Manfred Steger.

3.2 Summary Table

Table 3.1: Types of research questions
Type Category Description Example
Qualitative Exploratory Seeks to understand perspectives and meanings without influencing results. What are people’s thoughts on the new government?
Qualitative Interpretive Examines individuals’ behaviour and cultural meaning in natural settings. How do researchers perceive AI’s role in academic publishing?
Qualitative Experiential Focuses on understanding individuals’ lived experiences and perspectives. What are the challenges students face during their transition from school to college?
Quantitative Descriptive Measures responses of a population regarding a particular question or variable. How often do students use online learning platforms?
Quantitative Exploratory (EDA) Uses statistical and visual methods to summarize dataset characteristics. What patterns can be observed in student performance over the last decade?
Quantitative Comparative Investigates differences between two or more groups regarding an outcome variable. How do attitudes toward online learning differ between visual and kinesthetic learners?
Quantitative Correlational Examines relationships between two or more variables without implying causation. What is the relationship between disposable income and job satisfaction?
Quantitative Predictive Uses statistical models to estimate future trends or outcomes. Can student attendance predict final grades?

In research a distinction is made between qualitative and quantitative research methods. Sometimes the design incorporates both types of methods known as the mixed method. This distinction is primarily because of the type of data that is collected, and the methods used for analysis of the data. In this book, we will primarily look at the quantitative aspects of data analysis.

Many methods of quantitative analysis of data which are statistical in nature were developed in the last hundred years. Qualitative methods are more recent in origin. Another aspect of the data analysis which has really prospered in recent times is improvement in data visualisation methods. R has excellent libraries for both aspects. We will look at various concepts statistical analysis using R, i.e. all computations will be made using R and in the process introduce the syntax, functions and libraries of R.

Exercise

What is the kind of research question (RQ) that you have?

I am assuming a basic familiarity with some basic statistical concepts. But in any case we will try to be thorough with the conceptual understanding of the topic at hand and its applications.

I will try to provide historical context for the development of these ideas when possible. We will use some of the inbuilt datasets in R for discussion some concepts and some datasets that were derived from various sources.

3.3 What is R?

R is a programming language for statistical computing, data analysis and graphics. Open-source and widely used in data analysis, statistics, and machine learning in both academic and industry. R is particularly powerful for data visualisation and statistical modelling. R is highly extensible due a vast ecosystem of packages/libraries (over 18,000 on CRAN) for specialised tasks, so if there is a need for a particular task chances are that there is already a library for it. Finally R is cross-platform, it runs on Windows, macOS, and Linux, making it accessible to users on any operating system. Added advantage is that same programme will run across computers.

R was developed in the early 1990s by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. It was inspired by the S programming language developed at Bell Labs in the 1970s. R was officially released as Free Software under the GNU General Public License (GPL) in 1995. Because R is Free Software, the users don’t have to pay for its use or distribution.

Growth and Popularity

The Comprehensive R Archive Network (CRAN) was established in 1997 to distribute R packages. R gained popularity in data science, statistics, and academia due to its flexibility and vast package ecosystem. Today, R is used by millions of statisticians, data scientists, and researchers worldwide. Major corporations, now use R for data analysis.

Tidyverse Revolution: In the 2010s, Hadley Wickham and colleagues developed the tidyverse, a collection of R packages for data science

RStudio: The development of RStudio (now Posit) in 2011 made R more accessible with a user-friendly development environment. We will use R Studio in this book. R Studio is released under AGPL 3.

A large community of R users in various fora, resources and tutorials exists which makes getting help easy.

3.4 Why Use/Learn R?

Flexible scripting capabilities for automation and reproducibility. R can also use data from many sources and formats such as csv, xlsx, spss etc. R also provides support for mathematical typesetting using LaTeX. R Studio also provides us with options to create full reports, books, presentations and interactive visualisations and dashboards. It is also possible to export them to various formats like HTML, EPUB, PDF and DOCX or even create websites. This book was itself written in R Studio using the bookdown library of R.

3.5 Some Examples of R

3.6 Installing R and RStudio

To start using R via R studio, you will need to first install R and then R Studio. Please remember the order of installation. The RStudio Website provides links for both installations.

3.7 First Steps in R

At the first glance the R Studio interface looks a bit intimidating. There seem to be too many options with no clear indication of how to use it. Don’t worry, we will start using some basic commands. I will highly recommend use of R Markdown (RMD) or equivalently Quatro Markdown (QMD) format for using R Studio. With QMD (or RMD) format you can write text and code in the same file and see the results of the code (such as numbers, tables or graphs) in the final output.

You can refer to the R Studio cheatsheet here to get a basic orientation of the interface.

The R Studio interface has four panes. The basic idea of programming with R Studio is that you write the code in the upper left corner window (the editor) and its output will be see in the lower left terminal, or in the lower right window.

The windows and their placement is configurable in the Seetings.

Markdown Format

Markdown is a simple document format which allows us to write structured documents using simple text. For example, to create a section in the document we use the # for a subsection we use ## etc. Table Table 3.2 shows some basic commands of the markdown format.

Table 3.2: Basic Markdown Formatting
Feature Markdown Syntax
Heading 1 # Heading 1
Heading 2 ## Heading 2
Heading 3 ### Heading 3
Bold **Bold Text**
Italic *Italic Text*
Bold + Italic ***Bold Italic***
Unordered List - Item 1
- Item 2
Ordered List 1. First
2. Second
Inline Code `code`
Code Block (R) ```{r}
plot(1:10)
```
Blockquote > Quoted text
Links [Text](https://example.com)
Images ![Alt Text](image.png)
Tables | Col1 | Col2 |
|------|------|
| Data | Data |

The reason I am introducing this first so that you can create documents in R Studio which you will work on as we progress through the book.

You can see this excellent primer on using R Studio and R Markdown format.

Some more useful commands in RMD format are given in the cheatsheet.

3.8 Base R functions

Some functions in R known as base-R functions which are always available when you start R. They provide essential tools for data manipulation, statistical analysis, and visualization. For example, basic arithmetic operators like + - * \ are always available. Also some of the plotting functions like plot() and some statistical/mathematical functions like mean(), sum() are always available. For more extended functionality, R has different packages which provide specific functions.

Another way to look at this is that base-R functions are like a basic car model. To get the additional functionality we need to get other things installed on this base model of the car. Similarly, in R we need to install libraries to get additional functions.

Code Blocks

We type commands in R at the window known as the console. You will have the > as prompt. All the messages pertaining to the command execution, including the error messages, will appear here. If the output of the command is a graphic, it will appear in the Viewer window on right hand lower side. Similarly any data sets and their summaries will appear in the Environment window (right hand top side).

You can insert codes in a markdown file which can run the code and display its output. For example, the code block below shows how to install packages in R.

# Command to install R packages
# This is how you add comments in a code block
# Note that the package name is in double quotes

install.packages("rmarkdown")

Do not forget to add the quotation marks around the package name. Also note that R Studio has an auto-complete feature so that it can help you write the code as well. Now you know how to install new packages in R! We will install other packages as required. To get help on any package you can click and search in the Help window in the Viewer part of R Studio. If you do not add quotes the command will throw an error. It is essential to also understand the errors that we get when R syntax is not correct.

Using R Studio and R markdown (RMD) format we typically write our code in special sections in the RMD file. These sections are called as “chunks”. There are “text chunks” (like this one) and “code chunks” like what we have see. A code chunk allows you to write and execute R code inside the Markdown document. You can name the chunk, set options for evaluation, control what gets displayed, and much more. Below is a simple example with the expression 5 + 3, where we will see different aspects of the R code chunk.

Let us look at some simple variations on how this simple example will give different outputs in the rmd file depending on the options that we give to the code chunk.

The code chunk will start and end with three quotes “```”. After this you need to specify the language we are using for this chunk in our case “r”, and name of the chunk. There are other options which we explore below.

Code Chunk Variations

This chunk will be visible and will be evaluated. The result will be given in the output.

5 + 11
[1] 16

This chunk will not be evaluated

5 + 3

This chunk will be evaluated, but code will not be displayed

[1] 8

See the corresponding output in the HTML file.

Exercise

Try these commands and see if the output is correct.

5*8
5^8

3.9 Tidyverse

One of the most powerful packages in R is the tidyverse package. Tidyverse is actually a collection of eight packages. We will get several packages with tidyverse including very powerful ggplot2 and dplyr packages. If any other packages are required we will install them later.

Install the tidyverse package using the command above.

You can have a look at the various components of the tidyverse here.

After installing the package you will need to “load” the package to the current working space. Without loading the package to the current working space you cannot use it. The syntax to load a package is as follows:

library("rmarkdown")
library("tidyverse")

Exercise

Load the tidyverse, package using the above command. If you have not installed tidyverse you will get an error.

At this point I want to clarify a confusion that I have often seen about installing packages and loading them. You have to install the packages only once on your system (this is done using the install.package() command. When you close R Studio the packages will remain installed, you do not need to install them again. But each time you restart R Studio you will need to load the packages using library()each time so that they are accessible to you. If you do not load the packages the commands of that package will not be accessible to you and you will get errors while running those commands. We will see some examples of the typical errors when we start coding.

In the next chapter we will look at what is data, its types and how to import and “see” data in R.