Quantitative Research – Key Vocabulary

Vocabulary flashcards covering core terms, processes, characteristics, strengths, weaknesses, and designs related to quantitative research.

Inquiry

An investigation that involves asking questions to probe or examine a topic or problem.

Research

A systematic, scientific process of solving problems, generating new knowledge, and validating information.

Scientific Method

Step-by-step procedure (problem, hypothesis, data collection, analysis, conclusion) used to conduct research objectively.

Research Problem

An unfavorable condition or question that guides the focus of a study; considered the heart of the investigation.

Hypothesis

A testable prediction that directs the research design and data analysis.

Null Hypothesis (H₀)

Statement asserting no significant relationship or difference between variables.

Alternative Hypothesis (H₁)

Statement proposing a significant relationship or difference between variables.

Data Collection

The systematic gathering of information to support or refute a hypothesis.

Data Analysis

Examining collected data—often with statistical tests—to evaluate preconceived hypotheses.

Data Interpretation

Making sense of analyzed data to draw meaningful implications and relevance.

Reporting Results

Communicating findings so stakeholders can apply them; a core researcher responsibility.

Quantitative Research

Research that measures variables numerically, analyzes them statistically, and reports relationships among variables.

Qualitative Research

Research that explores phenomena through words or images, emphasizing meaning and context rather than numbers.

Variable

Any measurable characteristic or factor that can vary among study participants.

Independent Variable

The variable manipulated or categorized to observe its effect on another variable.

Dependent Variable

The outcome variable measured to assess the effect of the independent variable.

Random Sampling

Selecting participants so every member of the population has an equal chance of inclusion, minimizing bias.

Standardized Instrument

A pre-tested tool (survey, test, scale) used uniformly to ensure accuracy, reliability, and validity of data.

Reliability

The consistency or repeatability of measurement results across time or observers.

Validity

The degree to which an instrument measures what it is intended to measure.

Population

The entire group to which research results are intended to generalize.

Sample

A subset of the population actually studied to draw conclusions.

Measurable Variables

Numeric indicators such as age, gender, scores—central to quantitative data gathering.

Figures, Tables, Graphs

Visual summaries that display trends, relationships, or differences among numerical data.

Large Random Samples

Sizeable participant groups chosen randomly to enhance reliability of findings.

Replicability

Capacity to repeat a quantitative study in another setting to verify findings.

Emphasis on Proof

Quantitative focus on confirming or refuting pre-set hypotheses rather than open discovery.

Causal Relationship

Connection showing how changes in one variable produce effects in another.

Hypothesis Testing

Statistical procedure to decide whether to accept or reject a stated hypothesis.

Representative Sample Assumption

Belief that the sample accurately reflects the broader population’s characteristics.

Objectivity

Research stance that minimizes personal bias; data drive conclusions.

Objective Answers

Quantitative results expressed in numbers rather than opinions.

Reliable Results

Findings that would be consistent if the study were repeated under similar conditions.

Pre-existing Instruments

Already validated tools available for quick deployment in data collection.

Bias Minimization

Design elements (e.g., randomization) that keep personal influence out of the results.

Fast Data Gathering & Analysis

Use of surveys and statistical software to speed up quantitative workflows.

Context Ignored

Weakness where situational factors are overlooked because numbers dominate interpretation.

High Cost

Potential expense of large-scale data collection and specialized software in quantitative studies.

Limited Results Scope

Numerical findings may not capture complex human experiences fully.

Less Elaborate Human Perception

Quantitative data provide briefer accounts of feelings or motivations compared to qualitative data.

Non-numeric Information Gap

Certain phenomena cannot be adequately expressed through numbers alone.

Experimental Design

Research strategy involving manipulation of variables to determine cause-and-effect.

Control Group

Participants who do not receive the treatment; baseline for comparison.

Experimental (Treatment) Group

Participants who receive the intervention to assess its impact.

Manipulation

Deliberate change of the independent variable to observe its effect.

Random Assignment

Placing subjects into groups by chance to equalize characteristics across conditions.

True Experimental Design

Uses random assignment, control group, and manipulation—highest internal validity.

Quasi-Experimental Design

Includes control group and manipulation but lacks random assignment.

Pre-Experimental Design

Employs manipulation without random assignment or a true control group.

Correlational Design

Measures the degree of association between two or more variables without manipulation.

Survey Design

Employs questionnaires to identify trends in attitudes, opinions, or behaviors of a population.

Ex Post Facto Design

Compares groups based on pre-existing conditions; variables are not manipulated.

Non-experimental Research

Studies that do not manipulate variables; includes survey, correlational, ex post facto, etc.

Deductive Approach

Reasoning from general theory to specific hypotheses; common in quantitative studies.

Generalizability

Extent to which study findings apply to the wider population.

Statistical Analysis

Mathematical techniques applied to numerical data to test hypotheses and interpret relationships.