Glossary of Research Methodology Terms

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This glossary provides clear, concise definitions of essential research methodology terms organized alphabetically. Use it as a quick reference when reading academic papers, writing your thesis, or preparing for examinations.

A

Abstract: A concise summary (150-300 words) of a research paper covering purpose, methods, findings, and conclusions.

Alpha level (α): The threshold probability for rejecting the null hypothesis, typically set at 0.05 (5% chance of Type I error).

Alternative hypothesis (H₁): The statement that an effect or relationship exists; what the researcher believes to be true.

ANOVA (Analysis of Variance): Statistical test comparing means across three or more groups using the F-statistic.

B

Beta (β): The probability of Type II error (failing to detect a real effect). Also used for standardized regression coefficients.

Bias: Systematic error that distorts findings in a particular direction, distinct from random error.

Blind/Blinding: Concealing group assignments from participants (single-blind) or both participants and researchers (double-blind) to reduce bias.

C

Case study: In-depth investigation of a single entity (person, organization, event) within its real-life context.

Causation: A relationship where one variable directly produces change in another. Requires temporal precedence, correlation, and elimination of alternative explanations.

Coefficient of determination (R²): Proportion of variance in the dependent variable explained by the independent variable(s).

Confidence interval (CI): A range of values likely to contain the true population parameter, typically expressed at 95% confidence.

Confounding variable: A variable that influences both the IV and DV, creating a spurious relationship between them.

Construct validity: The degree to which a test measures the theoretical construct it claims to measure.

Control group: A group that does not receive the treatment/intervention, serving as a baseline for comparison.

Convenience sampling: Selecting participants based on accessibility rather than random selection.

Correlation: A statistical relationship between two variables; does NOT imply causation.

Cronbach's alpha (α): Measure of internal consistency reliability for multi-item scales (acceptable ≥ 0.70).

Cross-sectional study: Data collected at a single point in time from a sample representing various groups.

D

Data saturation: Point in qualitative research where new participants yield no new themes or insights.

Deductive reasoning: Moving from general theory to specific predictions (hypothesis testing).

Degrees of freedom (df): The number of values free to vary in a statistical calculation.

Dependent variable (DV): The outcome variable measured to assess the effect of the independent variable.

Descriptive statistics: Statistics that summarize data (mean, median, SD, frequencies) without making inferences.

E

Effect size: A standardized measure of the magnitude of a finding (e.g., Cohen's d, η², R²).

Empirical research: Research based on systematic observation or experiment rather than theory alone.

Epistemology: The branch of philosophy concerned with the nature and sources of knowledge.

Ethics committee (IRB): Institutional body that reviews and approves research involving human participants.

Ethnography: Qualitative research method involving extended immersion in a cultural group to understand their practices and beliefs.

External validity: The extent to which findings can be generalized to other populations, settings, or times.

F-G

Factor analysis: Statistical technique identifying underlying dimensions (factors) from a set of observed variables.

Falsifiability: The possibility that a hypothesis can be proven wrong through empirical evidence.

Generalizability: The extent to which findings apply beyond the specific study sample and context.

Grounded theory: Qualitative methodology that develops theory systematically from data rather than testing pre-existing theory.

H-I

Hypothesis: A testable prediction about the relationship between variables.

Independent variable (IV): The variable manipulated or examined as a potential cause/predictor.

Inductive reasoning: Moving from specific observations to broader generalizations or theories.

Inferential statistics: Statistical methods that draw conclusions about populations from sample data.

Informed consent: The process of ensuring participants understand and voluntarily agree to participate in research.

Internal validity: The confidence that observed effects are caused by the IV rather than confounding factors.

L-M

Likert scale: Response scale (typically 5 or 7 points) measuring agreement/disagreement with statements.

Longitudinal study: Research collecting data from the same subjects over multiple time points.

Mean: The arithmetic average of a set of values.

Median: The middle value when data is arranged in order.

Mediating variable: A variable that explains the mechanism through which an IV affects a DV.

Meta-analysis: Statistical technique combining results from multiple studies to calculate a pooled effect estimate.

Mixed methods: Research combining quantitative and qualitative approaches in a single study.

Moderating variable: A variable that changes the strength or direction of the IV-DV relationship.

N-O

Normal distribution: A symmetric, bell-shaped probability distribution (mean = median = mode).

Null hypothesis (H₀): The statement that no effect or relationship exists; the default assumption.

Objectivity: The extent to which research is free from researcher bias and personal influence.

Ontology: The branch of philosophy concerned with the nature of reality and existence.

Operationalization: Defining abstract concepts in terms of specific, measurable indicators.

P-Q

P-value: The probability of obtaining results as extreme as observed, assuming the null hypothesis is true.

Paradigm: A worldview or framework of beliefs and assumptions guiding research practice.

Phenomenology: Qualitative approach studying the lived experience of a phenomenon from participants' perspective.

Pilot study: A small-scale preliminary study to test procedures, instruments, and feasibility before the main study.

Population: The entire group of individuals to which the researcher wants to generalize findings.

Power (statistical): The probability of detecting a real effect when one exists (1 - β; typically ≥ 0.80).

Purposive sampling: Deliberately selecting participants who possess specific characteristics relevant to the study.

Qualitative research: Research exploring meanings, experiences, and processes through non-numerical data (interviews, observations, documents).

Quantitative research: Research measuring variables numerically and analyzing them statistically to test hypotheses.

R-S

Random assignment: Allocating participants to experimental conditions using chance procedures.

Random sampling: Selecting participants from a population where every member has an equal chance of selection.

Reliability: The consistency and stability of a measurement instrument across repeated applications.

Replication: Repeating a study to verify findings and assess generalizability.

Sample: A subset of the population selected for study.

Sampling frame: The complete list of population members from which a sample is drawn.

Significance (statistical): The probability that observed results are not due to chance alone (typically p < 0.05).

Standard deviation (SD): The average distance of data points from the mean.

Systematic review: A structured, reproducible review of all available evidence on a specific research question.

T-V

Triangulation: Using multiple methods, sources, or perspectives to strengthen research findings.

Type I error: Incorrectly rejecting a true null hypothesis (false positive).

Type II error: Failing to reject a false null hypothesis (false negative).

Validity: The extent to which a study or instrument measures what it claims to measure.

Variable: Any characteristic that can take different values across observations.

Conclusion

This glossary covers the most essential terms you will encounter throughout your research methodology studies. For deeper understanding of any concept, refer to the relevant detailed chapter in this resource.