Chapter 2: Types of Data, How to Collect Them & More

Vocabulary flashcards covering key definitions, sampling methods, research designs, measurement scales, and statistical analysis types from Chapter 2.

Variable

Characteristic of the population (e.g., age, height, stress level).

Value

Specific measurement or number associated with a variable (e.g., 4, 81, 367.12).

Population

Entire group of interest (e.g., all college students in the U.S.).

Sample

Subset of the population (e.g., 40 students from a community college).

Statistic

Data from a sample (e.g., heights of 40 students).

Parameter

Data from a population (e.g., heights of all students).

Representative Sample

Matches the population characteristics.

Unrepresentative (Biased) Sample

Does not match the population characteristics.

Random Sampling

Selection from a complete list of the population.

Simple Random Sampling

Equal chance for every population member.

Stratified Random Sampling

Samples from different subgroups (e.g., based on ethnicity).

Convenience Sampling

Sample from individuals who are easy to access.

Observational Studies

Collect data through observation without intervention.

Non-Experimental (Correlational) Design

Observing naturally occurring characteristics (e.g., height, weight, age).

True Experiment

Manipulates variables to determine cause-and-effect (e.g., drug vs. placebo).

Independent Variable (IV)

The variable manipulated by the researcher.

Dependent Variable (DV)

The outcome measured in response to the IV.

Quasi-Experimental Design

Similar to experimental but lacks random assignment (e.g., comparing males and females).

Qualitative (Categorical) Variables

Express attributes (e.g., eye color, religion).

Quantitative (Numerical) Variables

Measured numerically (e.g., height, weight).

Nominal Scale

Categories without order (e.g., favorite color).

Ordinal Scale

Ordered categories (e.g., satisfaction levels).

Interval Scale

Numerical values without a meaningful zero (e.g., temperature).

Ratio Scale

Numerical values with a meaningful zero (e.g., income).

Construct

Abstract characteristics (e.g., intelligence).

Reliability

Consistency of results.

Validity

Accuracy in measuring what is intended.

Descriptive Statistics

Summarizes data using calculations, graphs, or tables.

Inferential Statistics

Makes predictions or generalizations about a population based on a sample.

Summation Notation

Greek letter $\Sigma$ indicates summation (e.g., adding scores).