Glossary

This glossary collects key terms used throughout the textbook. The definitions are intentionally brief and student-friendly. In the chapters, some terms are linked to this glossary so you can hover over the term and review the definition as you read.

A

Alternative Hypothesis: A hypothesis stating that there is an effect, difference, or relationship. In null hypothesis significance testing, evidence is evaluated against the null hypothesis rather than directly proving the alternative hypothesis.
Analysis: A statistical procedure or set of steps used to summarize, compare, model, or interpret data.

B

Bar Plot: A graph that displays counts or percentages for categories.
Between-subjects Design: A study design in which different people are in different conditions or groups. Each person contributes data to only one condition.
Binary Variable: A nominal variable with two categories, such as yes/no, present/absent, or control/treatment.
Box Plot: A graph that summarizes a distribution using the median, quartiles, and possible outliers.

C

Categorical Variable: A variable whose values represent categories or groups rather than quantities. Nominal and ordinal variables are categorical variables.
Composite Variable: A new variable created by combining two or more variables, often to represent an overall scale or construct.
Confounding Variable: A variable that is related to both the independent variable and the dependent variable, creating an alternative explanation for a result.
Continuous Variable: A variable whose values represent quantities or amounts. Continuous variables are often summarized with means and standard deviations.
Correlational Research: Research that examines whether variables are related without manipulating variables or randomly assigning participants to conditions.
Covariate: A variable included in an analysis to help account for differences related to the outcome variable.

D

Data Type: The kind of values stored in a variable, such as integer, decimal, or text.
Dataset: A structured collection of data, often organized in rows and columns.
Decimal: A numeric value that can include decimal places.
Density Plot: A smoothed graph showing the shape of a continuous variable’s distribution.
Dependent Variable: The outcome variable in a study. It is the variable measured to see whether it differs, changes, or is related to another variable.
Descriptive Statistics: Statistics used to summarize, organize, and describe the data in a sample.
Dichotomous Variable: A variable with two categories. Dichotomous variables are also often called binary variables.
Distribution: The pattern of values for a variable, including which values are common, which are rare, and how values are spread out.

E

Experimental Research: Research in which the researcher manipulates an independent variable and uses random assignment to place participants into conditions.

F

Frequency: The number of observations in a category or value.

G

Grouping Variable: A categorical variable that separates observations into groups for comparison.

H

Histogram: A graph that displays the distribution of a continuous variable by grouping values into intervals.
Hypothesis: A prediction about the answer to a research question.

I

ID Variable: A variable used to identify cases or observations, such as participant ID, rather than a variable usually analyzed statistically.
Independent Variable: A variable expected to explain, predict, influence, or cause differences in another variable. In an experiment, it is the variable manipulated by the researcher.
Inferential Statistics: Statistics used to make inferences from a sample to a larger population.
Integer: A whole number with no decimal places.
Interquartile Range: The range of the middle 50% of values in a distribution, calculated as the distance between the first quartile and third quartile.
Interval Variable: A continuous variable with equal spacing between values but no true zero point.

J

jamovi: A free, open-source statistical software program that uses menus and output tables to help users analyze data.
Jittered Data: Individual data points displayed with slight horizontal or vertical movement so overlapping points are easier to see.

K

Kurtosis: A description of the tail behavior of a distribution relative to a normal distribution.

L

Level: One category or value of a categorical variable, such as control and treatment.

M

Mean: The arithmetic average, calculated by adding all values and dividing by the number of values.
Mean Marker: A visual indicator showing the mean value on a graph.
Mean Split: A way of turning a continuous variable into categories by splitting values above and below the mean. This removes information from the original variable.
Measure Type: How jamovi understands a variable for analysis purposes, such as nominal, ordinal, continuous, or ID.
Median: The middle value when values are ordered from lowest to highest.
Median Split: A way of turning a continuous variable into categories by splitting values above and below the median. This removes information from the original variable.
Missing Value: A blank, code, or value indicating that data are missing, unavailable, skipped, or invalid.
Mode: The most frequently occurring value or category.
Module: An add-on in jamovi that provides additional analyses, tools, or features.

N

Nominal Variable: A categorical variable with categories that do not have a meaningful order.
Normal Distribution: A symmetric, bell-shaped distribution with values clustered around the center and fewer values farther from the center.
Null Hypothesis: A hypothesis usually stating that there is no effect, no difference, or no relationship.

O

Observation: One unit in a dataset, such as one participant, case, trial, or response. Observations are usually represented by rows.
Open-source Software: Software whose source code is openly available and can be inspected, modified, and improved by a community of users and developers.
Ordinal Variable: A categorical variable with categories that have a meaningful order, but not necessarily equal spacing between categories.
Outcome Variable: The variable being predicted, explained, or measured as the outcome in a study.
Outlier: An unusually extreme value compared with the rest of the data.
Output: The tables, statistics, graphs, or other results produced by an analysis.

P

Percentage: A proportion expressed out of 100, often used to describe how much of a sample falls in a category.
Percentile: A value below which a certain percentage of observations fall.
Population: The larger group, set of people, cases, or observations that a researcher wants to understand.
Predictor Variable: A variable used to predict or explain another variable.

Q

Quartile: One of the values that divides ordered data into four equal parts.
Quasi-experimental Research: Research that compares groups or conditions without random assignment.

R

R: A free programming language and software environment often used for statistical computing, data analysis, and data visualization.
Random Assignment: A procedure in which participants are assigned to conditions by chance.
Range: The difference between the maximum and minimum value.
Ratio Variable: A continuous variable with equal spacing between values and a true zero point.
Reliability: The consistency of a measure, such as consistency over time, across items, or across raters.
Repeated-measures Design: A design in which the same people are measured multiple times, such as across conditions or time points.
Reproducibility: The ability to check, repeat, or recreate an analysis using the same data, steps, and settings.
Reverse Scoring: Recoding an item so that high values become low values and low values become high values, usually so all items in a scale point in the same direction.
RStudio: An integrated development environment commonly used to write and run R code. RStudio is also often used to create Quarto documents and websites.

S

Sample: The smaller group, set of people, cases, or observations from which data are collected.
Scale Score: A score created by combining multiple items that are intended to measure the same construct, often using a total or average.
Shapiro-wilk Test: A statistical test used to evaluate whether a variable differs from a normal distribution.
Skew: Asymmetry in a distribution, where one tail is longer than the other.
Split By: A jamovi option that displays descriptives separately for each category of a grouping variable.
Standard Deviation: A measure of variability that describes how spread out values are around the mean, expressed in the original units of the variable.

T

Text: A data type containing words, labels, or other nonnumeric values.

V

Validity: The extent to which evidence supports the interpretation or use of a measure, result, or conclusion.
Variable: Anything measured, observed, manipulated, or recorded that can vary across observations.
Variable Description: A longer note explaining what a variable represents, how it was measured, or how it should be interpreted.
Variable Name: The short label used to identify a variable in a dataset.
Variance: A measure of variability based on squared deviations from the mean.
Violin Plot: A graph that displays the shape or density of a continuous variable’s distribution, often shown by group.

W

Within-subjects Design: A study design in which the same people are measured in more than one condition or at more than one time point.