Unit 1: One-variable data

controlled variables

Controlled variables, also called constant variables, refer to factors in an experiment that remain consistent throughout all conditions. By keeping these factors unchanged, researchers can isolate the effects of the independent variable on the dependent variable.

data set

A data set refers to any collection of observations, measurements, or information gathered for analysis.

dependent variables

Dependent variables are the outcomes or results that are being measured or observed in an experiment. They depend on the independent variable and can change as a result of its manipulation.

descriptive statistics

Descriptive statistics involves organizing, summarizing, and presenting data in a meaningful way to describe its main features.

element

An element refers to an individual unit or object in a population that is being studied.

experimental units

Experimental units are the individuals or objects on which we collect data in an experiment. They can be people, animals, plants, or any other entities that are being studied.

independent variables

Independent variables are factors or conditions that researchers manipulate or change in an experiment. They have a direct effect on the dependent variable and allow researchers to observe cause-and-effect relationships.

inferential statistics

Inferential statistics involves using sample data to make inferences or draw conclusions about a population.

observations

Observations are data points collected during an experiment or study, often involving measurements or recordings.

reliability

Reliability refers to the consistency and stability of a measurement or test over time, across different conditions, and among different raters.

respondents

Respondents are individuals who participate in surveys or questionnaires by providing answers to specific questions.

subjects

Subjects refer to the individuals or objects that are being studied or observed in a statistical experiment. They can be people, animals, plants, or any other entities of interest.

validity

Validity refers to the extent to which a measurement or test accurately measures what it is intended to measure.

variables

Variables are characteristics or attributes that can vary among individuals or objects in a study. They are the measurable quantities that researchers collect data on during an experiment or survey.

gender

categorical

race

categorical

marital status

categorical

employment status

categorical

education level

categorical

political party

categorical

religion

categorical

eye color

categorical

hair color

categorical

birthplace

categorical

age

quantitative

height

quantitative

weight

quantitative

income

quantitative

body mass index (BMI)

quantitative

blood pressure

quantitative

heart rate

quantitative

hours of sleep

quantitative

distance traveled

quantitative

number of siblings

quantitative

categorical variable

A categorical variable is one that represents characteristics or qualities rather than numerical values. It consists of categories or groups into which data can be classified.

chi-square test

A statistical test used to determine if there is a significant association between two categorical variables. It compares the observed frequencies with the expected frequencies under the assumption of independence.

continuous

Continuous data refers to numerical data that can take on any value within a given range. It can be measured and divided into smaller units, and there are infinite possible values between any two points.

interval level of measurement

#3; named + ordered + proportionate interval between variables (quantitative)

nominal level of measurement

#1; named variables (non-mathematical)

ordinal level of measurement

#2; named + ordered variables (non-mathematical)

ratio level of measurement

#4; named + ordered + proportionate interval between variables + can accommodate absolute zero (quantitative)

t-test

A t-test is a statistical test that compares two sample means to determine if they are significantly different from each other.