Communications and Data analysis Exam 2

Variable

Any characteristic or attribute that can take on different values (e.g., age, gender, attitudes)

Independent Variable (IV)

The variable manipulated or controlled by the researcher; presumed cause.

Dependent Variable (DV)

The variable being measured or affected; presumed effect.

Control Variable

A variable held constant to isolate the relationship between IV and DV.

Extraneous Variable 

An outside factor that may influence results but is not of interest to the study

Confounding Variable

 An extraneous variable that systematically varies with the IV and could distort results

Operational Definition

A clear, specific explanation of how a variable is measured or manipulated.

Conceptual Definition

The abstract, theoretical meaning of a variable.

Continuous Variable

A variable that can take on any value within a range (e.g., age, income).

Categorical Variable 

A variable with distinct groups or categories (e.g., gender, ethnicity)

Moderator Variable

A variable that affects the strength or direction of the relationship between IV and DV.

Mediator Variable

Explains how or why an IV affects a DV.

Hypothesis

A specific, testable prediction about the relationship between variables.

Null Hypothesis (H₀)

States there is no relationship or difference between variables.

Alternative Hypothesis (H₁)

States there is a relationship or difference between variables.

Directional Hypothesis 

Predicts the direction of the relationship (e.g., “increases,” “decreases”)

Non-Directional Hypothesis

Predicts a relationship exists but not its direction.

Research Question (RQ) 

A broad question guiding the study, often used when not enough theory exists to form a hypothesis

Purpose Statement

Explains why the study is being conducted; identifies variables and population.

Theory

A set of related concepts that explains or predicts phenomena.

Deductive Reasoning

Moving from theory → hypothesis → data testing.

Inductive Reasoning

Moving from data → patterns → theory building.

Population

The entire group the researcher wants to understand.

Sample

A subset of the population actually studied.

Sampling Frame

A list or representation of all elements in the population.

Probability Sampling 

Every member of the population has an equal chance of being selected.

Nonprobability Sampling 

Not all members have an equal chance; may introduce bias.

Simple Random Sampling

Each individual has an equal, random chance of selection.

Systematic Sampling

Selecting every kth person from a list.

Stratified Sampling

Dividing population into subgroups (strata) and sampling from each.

Cluster Sampling

Sampling entire groups or clusters rather than individuals.

Convenience Sampling 

Using participants who are easily accessible.

Purposive Sampling

Selecting participants based on specific characteristics or purpose.

Snowball Sampling

Existing participants recruit others from their network.

Sampling Bias

When the sample doesn’t accurately represent the population.

Sample Size (n)

The number of participants in the study; affects statistical power.

Measurement 

The process of assigning numbers or labels to variables according to rules

Reliability

The consistency or stability of a measure.

Validity

The accuracy of a measure (does it measure what it claims to?)

Internal Validity 

The extent to which results are due to the IV and not confounding factors

External Validity 

The extent to which results can generalize to other settings or populations.

Face Validity

Whether the measure looks like it measures the construct.

Content Validity

Whether the measure covers all aspects of the concept.

Construct Validity 

Whether the measure relates to other variables as theoretically expected

Criterion Validity 

Whether the measure predicts outcomes it should (e.g., SAT scores predicting GPA).

Test–Retest Reliability

Consistency of results over time.

Inter-Rater Reliability

Agreement between different observers.

Internal Consistency

How well items on a scale measure the same construct.

Likert Scale

A scale asking the degree of agreement (e.g., strongly agree → strongly disagree).

Semantic Differential Scale 

Measures meaning using bipolar adjectives (e.g., “good–bad,” “friendly–hostile”).

Nominal Level 

Categories with no order (e.g., gender).

Ordinal Level

Categories with order but unequal intervals (e.g., class rank).

Interval Level

Equal intervals, no true zero (e.g., temperature).

Ratio Level

Equal intervals with a true zero (e.g., age, income).

Survey

A structured method for collecting self-report data from respondents.

Questionnaire

A written set of questions for collecting survey data.

Closed-Ended Question

Provides pre-determined response options.

Open-Ended Question

Allows respondents to answer in their own words.

Leading Question

Suggests a particular answer (should be avoided).

Double-Barreled Question 

Asks two things at once (e.g., “How satisfied are you with your pay and benefits?”).

Loaded Question

Contains assumptions that may bias the response.

Social Desirability Bias

When respondents answer in a way they think is favorable to others.

Question Order Effect

The influence that earlier questions have on later responses.

Pilot Test

A trial run to test survey clarity and reliability.

Response Rate 

The percentage of people who complete the survey out of those contacted.

Cross-Sectional Survey

Data collected at one point in time.

Longitudinal Survey

Data collected at multiple points over time.

Self-Administered Survey

Participants complete it on their own (e.g., online, mail).

Interviewer-Administered Survey

Conducted by an interviewer (e.g., phone, in person).

Skip Logic 

Directs respondents to different questions based on previous answers.

Likert-Type Item 

A single question using agreement/disagreement scaling.