Quantitative Research Methods

Quantitative Research

A research strategy that uses systematically coded, numeric information to compare cases, measure magnitudes, and analyze social patterns[3][10].

Research Methods

The technical tools and procedures used to gather and analyze empirical data (e.g., surveys, administrative datasets, coding sheets)[4].

Methodology

The broader theoretical and philosophical reasoning that explains why a specific method fits a research question and what inferences can be drawn[4].

Deductive Reasoning

A top-down reasoning process that tests pre-existing theoretical propositions by deducing specific hypotheses and evaluating them with empirical data[1][12].

Inductive Reasoning

A bottom-up reasoning process that builds new theoretical concepts and generalizations directly out of observed empirical patterns[1][12].

Iterative Process

A research workflow characterized by moving back and forth between empirical data and theoretical ideas as unexpected findings emerge[1][13].

Epistemology

The philosophical study of knowledge—specifically how we know what we claim to know and what counts as valid knowledge[15].

Positivism

An epistemological stance advocating the application of natural science methods to the study of social reality, emphasizing empirical observation, hypothesis testing, and intersubjectivity[10][16].

Empiricism

The foundational scientific principle that claims must be subjected to rigorous observation and sensory evidence before being accepted as knowledge[16].

Intersubjectivity

The criterion that research procedures and data must be sufficiently transparent so that different researchers, regardless of personal values, can inspect and evaluate the work[15][16].

Interpretivism

An epistemological stance asserting that social researchers must grasp the subjective, empathetic meanings human actors assign to their social world[10][15].

Ontology

The philosophical study of the nature of social reality and existence[10][17].

Objectivism

An ontological stance asserting that social entities exist as objective realities external to, and independent of, social actors[10].

Constructionism

An ontological stance asserting that social entities and realities are continually produced and revised through social interaction[10].

Empirical Claim

A verifiable statement describing observable facts about how the social world operates[16].

Normative Claim

A value-based judgment asserting how the social world should or ought to be, which cannot be tested through empirical observation[16].

Overgeneralization

A reasoning error where an individual case or anecdote is mistakenly assumed to apply universally[7][8].

Selective Observation

A reasoning error where an observer pays attention only to cases that confirm their pre-existing beliefs while ignoring counter-evidence[7].

Illogical Reasoning

Jumping prematurely from an observed pattern to a causal conclusion without ruling out rival explanations[9].

Resistance to Change

Unwillingness to revise beliefs when faced with new empirical evidence due to tradition, authority, or personal identity[9].

Descriptive Claim

An empirical statement quantifying the amount, frequency, or distribution of a single variable[18].

Associational Claim

An empirical statement asserting that two or more variables co-occur or correlate in a predictable pattern[18].

Causal Claim

An empirical statement asserting that a change in one variable directly produces a change in another variable

Question Funnel

The systematic process of filtering a broad interest area down into a narrow, specific research question by defining the population, concepts, comparison, and data [Week 2 .pdf].

Gary Marx Question Triggers

A set of real-world and academic sources for research questions, including gaps between official claims and ground facts, theoretical puzzles, and new social/technological trends [CH 15 reading.pdf, Week 2 .pdf].

Normative Claim

A value-based judgment stating how society or institutions ought or should behave, which cannot be verified through empirical data alone [Week 2 .pdf].

Empirical Claim

A verifiable statement describing observable facts, patterns, associations, or causal effects in the social world [Week 2 .pdf].

Descriptive Claim w2

A basic empirical statement quantifying the frequency, rate, or percentage distribution of a single variable [Week 2 .pdf].

Associational Claim w2

An empirical statement asserting that two or more variables co-occur or correlate in a predictable statistical pattern [Week 2 .pdf].

Causal Claim w2

An empirical statement asserting that a change in one independent variable directly produces a change in a dependent variable [Week 2 .pdf].

Claims Ladder

The conceptual progression from basic Description, to finding Association, to demonstrating Causation, where each higher step requires stronger design controls and evidence [Week 2 .pdf].

Literature Matrix

A grid or table used to extract, synthesize, and compare key methodological details (sample, variables, findings, limitations) across multiple academic studies [Week 2 .pdf].

Saturation Rule

The point in a literature review when new database searches return duplicate studies and no new theoretical concepts or findings appear [Week 2 .pdf].

Search Replication / Audit Trail

The requirement that a literature search strategy (keywords, databases, Boolean strings, filters) be recorded transparently so another researcher could reproduce the search [Week 2 .pdf].

Tri-Council Policy Statement (TCPS2)

The Canadian ethical framework governing research involving humans, funded by CIHR, NSERC, and SSHRC [CH 3 reading.pdf].

Research Ethics Board (REB)

An institutional committee tasked with reviewing research proposals involving human participants to ensure compliance with ethical standards [CH 3 reading.pdf, Week 3.pdf].

Respect for Persons

TCPS2 principle requiring that participants be treated with dignity as autonomous decision-makers through free, informed, and ongoing consent [CH 3 reading.pdf, Week 3.pdf].

Concern for Welfare

TCPS2 principle requiring researchers to protect the physical, mental, emotional, social, cultural, and informational well-being of participants and communities [CH 3 reading.pdf, Week 3.pdf].

Justice

TCPS2 principle requiring the fair and equitable distribution of research risks and benefits, ensuring vulnerable groups are neither exploited nor excluded [CH 3 reading.pdf, Week 3.pdf].

Informed Consent

A voluntary, ongoing process where prospective participants are fully informed about a study's purpose, tasks, risks, benefits, and data storage before agreeing to participate [CH 3 reading.pdf, Week 3.pdf].

Institutional Coercion

Subtle pressure in captive or hierarchical settings (e.g., prisons, probation, schools) that makes refusal feel unsafe or non-voluntary [CH 3 reading.pdf, Week 3.pdf].

Privacy

An individual's right to control access to themselves, their personal space, and their private information [CH 3 reading.pdf, Week 3.pdf].

Confidentiality

The ethical condition where the researcher knows the identity of the participant but actively protects that identity from public disclosure [CH 3 reading.pdf, Week 3.pdf].

Anonymity

The ethical condition where data is collected without any direct or indirect identifiers, making it impossible for anyone (including the researcher) to link responses back to an individual [CH 3 reading.pdf, Week 3.pdf].

Limits to Confidentiality

Legal or professional duties that require breaking confidentiality, specifically regarding imminent physical harm or child abuse disclosures [CH 3 reading.pdf, Week 3.pdf].

Direct Identifiers

Information that explicitly reveals a person's identity (e.g., name, student ID, email address) [Week 3.pdf].

Indirect Identifiers

Unique combinations of background variables (e.g., age, ethnicity, role, neighborhood) that allow re-identification of a participant [Week 3.pdf].

Unit of Analysis

The major entity or level of social life being measured and analyzed in a study (e.g., individuals, families, census tracts, court jurisdictions) [Week 3.pdf].

Ecological Fallacy

The logical error of making claims about individual-level behavior based solely on aggregate or group-level data [Week 3.pdf].

Reductionism (Individualistic Fallacy)

The logical error of explaining group-level patterns entirely through individual characteristics while ignoring structural context [Week 3.pdf].

First Nations Data Sovereignty

The right of First Nations to own, control, access, and possess data about their peoples, lands, services, and cultures [Week 3 corston reading.pdf, Week 3.pdf].

OCAP® Principles

First Nations data governance standards standing for Ownership, Control, Access, and Possession [Week 3 corston reading.pdf, Week 3.pdf].

Research Design

The overarching framework that connects a research question to empirical evidence, determining timing, comparison, control, and causal capability[2][3].

Data Collection Method

A specific technical procedure used to gather raw social observations (e.g., surveys, structured observation, administrative records)[2][3].

Nomothetic Explanation

An explanatory approach seeking general principles, statistical associations, or broad laws using variables across many cases[4][5].

Idiographic Explanation

An explanatory approach seeking a rich, empathetic, and detailed narrative account of a specific person, event, or sequence[4][5].

The Counterfactual Problem

The logical problem of determining what would have happened to the exact same unit if it had not been exposed to the independent variable[5].

Time Order

The causality criterion requiring that the cause (X) occurs prior to the effect (Y) in chronological sequence[7].

Non-Spuriousness

The causality criterion requiring that an observed relationship between X and Y cannot be explained away by a third confounding variable (Z)[7].

Confounder (Rival Explanation)

An unmeasured third factor that influences both the independent and dependent variables, creating a false or misleading association[9][10].

Mechanism

The intermediate process or pathway through which an independent variable produces a change in a dependent variable[8].

Internal Validity

The degree of confidence that a study's design demonstrates a true causal relationship without interference from rival explanations[11].

External Validity

The extent to which a study's findings can be generalized to other populations, settings, or real-world contexts[11].

History Threat

An internal validity threat where an outside event occurring concurrently with a study influences post-test outcomes[13][14].

Maturation Threat

An internal validity threat where participants naturally change, age, or develop over time[13][15].

Testing Threat (Sensitization)

An internal validity threat where taking a baseline pre-test alters participant awareness or subsequent answers[13][16].

Instrumentation Threat

An internal validity threat caused by changes in how measurement instruments or recording standards are applied over time[13][16].

Selection Bias

An internal validity threat where comparison groups differ systematically at baseline prior to the intervention[15].

Attrition (Mortality)

An internal validity threat caused by the non-random drop-out or disappearance of participants over time in longitudinal research[15].

Regression to the Mean

The statistical tendency for extreme baseline scores to move closer to the population average upon re-measurement[18].

Diffusion of Treatment

An internal validity threat where elements of an intervention leak into the comparison or control group[18].

Random Assignment

A random procedure used in true experiments to allocate participants to conditions, distributing both known and unknown confounders equally by chance[10].

Statistical Control

Analytical techniques used in non-experimental research to hold measured third variables constant while examining the relationship between X and Y[20][31].

Cross-Sectional Design

A design in which data on multiple variables is collected from cases at a single point in time[22].

Panel Study

A longitudinal design in which the exact same sample of cases is surveyed at multiple sequential time points

Conceptualization

The process of defining and clarifying the precise theoretical meaning and dimensions of an abstract concept.

Operationalization

The process of translating abstract concepts and dimensions into specific, observable indicators, questions, or coding rules.

Concept

An abstract theoretical category or idea that describes a phenomenon in the social world.

Dimension

A specific aspect, component, or sub-domain of a broader multi-dimensional concept.

Variable

A measurable attribute that can take on different values or categories across cases.

Indicator

An observable sign, survey question, or administrative record used to represent a concept empirically.

Index / Scale

A composite measure combining multiple indicators to capture a complex or multi-dimensional concept.

Nominal Level of Measurement

Categorical data with mutually exclusive categories that have no inherent rank or order.

Ordinal Level of Measurement

Categorical data with a logical rank order, but where mathematical distances between categories are unequal or unknown.

Interval Level of Measurement

Ranked data with equal mathematical distances between values, but lacking a true, non-arbitrary zero point.

Ratio Level of Measurement

Ranked data with equal mathematical distances between values and a true, absolute zero point, permitting valid ratio claims.

Mutually Exclusive

A coding requirement ensuring that every case falls into one, and only one, category.

Exhaustive

A coding requirement ensuring that categories cover all possible responses or cases in the dataset.

Reliability

The consistency and stability of a measurement instrument across time, items, or observers.

Test-Retest Reliability

A method of assessing measurement stability by administering the same instrument to the same sample at two different time points.

Internal Consistency

The degree to which multiple items in a composite scale correlate with one another, often measured via Cronbach's alpha (α).

Inter-Observer Consistency

The degree of agreement between different coders or observers using the same measurement rules.

Measurement Validity

The extent to which an operational indicator accurately measures the specific concept it was designed to represent.

Face Validity

The degree to which a measure intuitively appears, at first glance, to capture the intended concept.

Concurrent Validity

A validation check comparing a new measure against an established benchmark measured at the same time.

Construct Validity

Evaluating a measure by assessing whether it relates to other variables in ways predicted by theory.

Convergent Validity

Assessing validity by comparing measures of the exact same concept derived through different data collection methods.

The Dark Figure of Crime

The volume of unreported and unrecorded crime that is omitted from official police statistics but uncovered through self-report victimization surveys.

Measurement Error

Inaccuracies introduced during data collection due to bad question wording, social desirability bias, or recall limitations.

Specification Error

A measurement flaw where the chosen indicator measures the wrong construct or omits core theoretical dimensions.