2.6 Putting It All Together
This chapter introduced the language of data. That language may feel basic, but it is the foundation for nearly everything else in this book.
Before running an analysis, you should be able to explain what your variables are, how they were measured, what your data look like, and whether you are describing a sample or making an inference about a population.
Chapter Recap
In this chapter, you learned that:
- data are organized into observations and variables
- variables can be categorical or continuous
- categorical variables can be nominal or ordinal
- continuous variables are often described as interval or ratio, but jamovi generally treats them as continuous
- descriptive statistics summarize the data we have
- inferential statistics use sample data to make claims about a larger population
- distributions show the shape and spread of values
- variability and outliers affect how we interpret data
- research design terms help us choose appropriate analyses and interpret conclusions carefully
A Foundational Decision Guide
When you encounter a new dataset or research question, start with these questions.
| Question | Why It Matters |
|---|---|
| What is the research question? | Helps you identify the purpose of the analysis |
| What are the variables? | Helps you know what was measured or manipulated |
| How was each variable measured? | Helps you identify the level of measurement |
| Is each variable categorical or continuous? | Helps you choose appropriate descriptive statistics, graphs, and tests |
| Are you describing a sample or making an inference about a population? | Helps distinguish descriptive from inferential statistics |
| What does the distribution look like? | Helps identify skew, variability, and outliers |
| What is the research design? | Helps you choose the right inferential test later |
This decision process will show up repeatedly throughout the textbook.
Applied Practice
Read each scenario and answer the questions that follow.
Scenario 1: Study Time and Exam Scores
A researcher asks 100 students how many hours they studied for an exam and records their exam score.
- What are the variables?
- Are the variables categorical or continuous?
- Is this primarily a descriptive or inferential question if the researcher only reports the average number of hours studied and average exam score?
- What might change if the researcher wants to know whether study time predicts exam scores among college students more broadly?
Suggested Answers
- Hours studied and exam score.
- Both are continuous variables.
- Descriptive, because the researcher is summarizing the sample.
- It becomes inferential if the researcher uses the sample to make a claim about a larger population of college students.
Scenario 2: Teaching Method and Quiz Performance
A researcher randomly assigns students to one of two teaching methods and then measures quiz performance.
- What is the independent variable?
- What is the dependent variable?
- Is the independent variable categorical or continuous?
- Is this an experimental, quasi-experimental, or correlational study?
Suggested Answers
- Teaching method.
- Quiz performance.
- Categorical, because students are assigned to one of two methods.
- Experimental, because the researcher randomly assigns students to conditions.
Scenario 3: Satisfaction Ratings
Students rate their satisfaction with a course using the following options: very dissatisfied, dissatisfied, neutral, satisfied, very satisfied.
- What level of measurement is this variable?
- Why is it not nominal?
- Why should we be cautious about treating it as continuous?
Suggested Answers
- Ordinal.
- It is not nominal because the response options have a meaningful order.
- The spacing between response options may not be equal, even if the categories are coded with numbers.
Check Your Understanding
- What is the difference between a nominal variable and an ordinal variable?
- Why do data need context before they can be interpreted?
- What is the difference between descriptive and inferential statistics?
- Why should researchers look at distributions before running statistical tests?
- Why does research design matter when choosing a statistical test?
Answers
- Nominal variables have categories with no meaningful order. Ordinal variables have categories with a meaningful order.
- A value only has meaning when we know what variable it belongs to, how it was measured, and what the values represent.
- Descriptive statistics summarize the data we have. Inferential statistics use sample data to make claims about a larger population.
- Distributions can reveal skew, variability, outliers, and other patterns that affect interpretation and assumptions.
- Research design helps determine whether groups are independent or repeated, whether causal claims are appropriate, and which statistical test fits the research question.
Why This Matters When You Use jamovi
These foundations are not just vocabulary. They affect what you do in jamovi.
If a variable is set up incorrectly, jamovi may offer the wrong analyses or hide the analysis you actually need. If a variable is categorical, frequencies and percentages may be more meaningful than a mean. If a variable is continuous, means, standard deviations, histograms, and boxplots may help you understand the data. If a variable needs to be reverse-scored, recoded, or combined with other variables, you need to prepare it before interpreting the results.
That is why the next few chapters focus on the practical workflow of opening data, preparing variables, describing them, and visualizing what you have.
Looking Ahead
Now that you have refreshed the language of data, the next chapter introduces jamovi, the software we will use to apply these ideas.
As you move forward, keep asking the same basic questions: What are my variables? How were they measured? What am I trying to learn from the data? Those questions will guide nearly every statistical decision you make.