Statistics and Variables in Research

These flashcards cover key vocabulary and concepts related to statistics and variables in research as discussed in the lecture.

Population

The group of people that researchers are interested in studying.

Sample

A subset of a population used to estimate the characteristics of the whole population.

Variable

Attributes/characteristics that can be measured and collected during the research process.

Qualitative Variable

A type of variable that can be categorical in nature, such as colors and foods.

Nominal Variable

A qualitative variable that has no order and no true zero.

Ordinal Variable

A qualitative variable that has an order and a true value, such as grades.

Quantitative Variable

A type of variable that is numerical in nature, which can be further classified into continuous and discrete.

Continuous Variable

A quantitative variable that has infinite values without an end.

Discrete Variable

A quantitative variable that has countable values.

Independent Variable (IV)

The controlled factor in an experiment that helps determine the effect.

Dependent Variable (DV)

The constant factor that is measured to assess how it changes because of the independent variable.

Descriptive Statistics

Methods used to summarize data to make sense of large amounts of information.

Mean

The average value of a dataset.

Median

The middle value of a dataset.

Mode

The frequently occurring value of a dataset.

Range

The difference between the maximum and minimum values in a dataset.

Standard Deviation

Indicates how spread out or close together the values of a dataset are from the mean.

Inferential Statistics

Methods used to make predictions or conclusions about a larger group based on data from a smaller group.

Normal Distribution

A way to spread out data that forms a bell-shaped curve.

Skewness

A measure of how much a dataset is uneven as opposed to being perfectly balanced.

Positive Skew

A distribution where most scores are low with very few high ones.

Negative Skew

A distribution where most scores are high with very few low ones.

Why use samples

Studying an entire population takes too much time and money; a well-chosen sample can provide accurate results.