AF 1406 Lecture 2: Defining and Collecting Data

Vocabulary flashcards covering key definitions, measurement scales, data sources, and sampling techniques from AF 1406 Lecture 2.

Variable

A characteristic of an item or individual.

Data

The set of individual values associated with one or more variables.

Statistic

A value that summarizes the data of a particular variable.

Random Variables

Variables that map outcomes of a random process to numbers.

Observation

A particular outcome of a random process.

Categorical Variable

A variable with values that can only be placed into categories, such as 'yes' and 'no'.

Numerical Variable

A variable with values that represent quantities.

Discrete Variable

A numerical variable whose values arise from a counting process.

Continuous Variable

A numerical variable whose values arise from a measuring process.

Types of Variables Classification

A hierarchical structure dividing variables into Categorical (Nominal, Ordinal) and Numerical (Discrete, Continuous).

Nominal Scale

A measurement scale that classifies data into distinct categories in which no ranking is implied.

Ordinal Scale

A measurement scale that classifies data into distinct categories in which ranking is implied.

Interval Scale

An ordered scale in which the difference between measurements is a meaningful quantity, but the measurements do not have a true zero point.

Ratio Scale

An ordered scale in which the difference between measurements is a meaningful quantity and the measurements have a true zero point.

Level of Measurement Properties

A summary table showing allowed mathematical operations (Frequency distribution, Median/percentiles, Addition/Subtraction, Mean/SD/SE, Ratios) across Nominal, Ordinal, Interval, and Ratio scales.

Population

Contains all of the items or individuals of interest that you seek to study.

Sample

Contains only a portion of a population of interest.

Population Parameter

A fixed, unchanging value that summarizes a specific variable for an entire population at a given time.

Sample Statistic

A value calculated from a sample used to estimate a population parameter, which changes with every new sample pulled due to sampling error.

Primary Sources

Data sources where the data collector is the same person or entity using the data for analysis.

Secondary Sources

Data sources where the person performing the data analysis is not the original data collector.

Sampling Frame

A listing of items that make up the population, such as lists, directories, or maps.

Non-Probability Sample

A sample in which items are chosen without regard to their probability of occurrence.

Convenience Sampling

A non-probability sampling technique where items are selected based solely on being easy, inexpensive, or convenient to sample.

Judgment Sample

A non-probability sampling method where opinions are collected from pre-selected experts based on their knowledge and judgment of the subject matter.

Probability Sample

A sample in which items are chosen on the basis of known probabilities.

Simple Random Sample

A probability sampling method where every individual or item from the sampling frame has an equal chance of being selected.

Systematic Sample

A sampling method where a frame of $N$ individuals is divided into groups of $k = \frac{N}{n}$ individuals, one individual is randomly selected from the first group, and every $k\text{th}$ individual thereafter is chosen.

Stratified Sample

A sampling method where the population is divided into two or more subgroups (strata) based on a common characteristic, and a simple random sample is selected from each subgroup.

Cluster Sample

A sampling method where the population is divided into several representative clusters, and a simple random sample of entire clusters is selected.

Strata vs. Cluster Comparison

A comparison of sampling groups: Strata are homogeneous inside and distinct between groups to maximize precision; Clusters are heterogeneous inside and similar between groups to maximize efficiency.