Quantitative and Qualitative Research Methods Flashcards

Comprehensive vocabulary flashcards covering basic terminology for quantitative analysis, qualitative data processes, mixed methods designs, and secondary data types.

Quantitative Analysis

Focuses on objective, measurable, numerical data to identify patterns, relationships, differences, and causal effects.

Independent Variable ($X$)

The predictor, cause, or factor of change in a research study.

Dependent Variable ($Y$)

The outcome being measured in a research study.

Confounding Variable

A hidden third factor that affects both the independent variable ($X$) and dependent variable ($Y$).

p-value

Used to assess statistical significance; $p \text{ ≤ .05}$ means findings are statistically significant and the null hypothesis is rejected.

Parametric Statistics

Statistics that assume normal distributions and use interval or ratio data.

Non-parametric Statistics

Flexible statistics used for nominal or ordinal data when parametric assumptions are not met.

Nominal Level

A level of measurement comprising categories only, such as gender or transport type.

Ordinal Level

A level of measurement comprising categories plus ranking, such as Likert scales.

Interval Level

Categories plus ranking and equal spacing, but with no true zero point, such as temperature.

Ratio Level

Interval properties plus a true zero point, such as income or years of experience.

Standard Deviation (SD)

The typical spread of data values around the mean.

Interquartile Range (IQR)

The spread of the middle $50$ of ordered data, calculated as $Q3 - Q1$.

Median

The middle value in a dataset; often preferred for skewed data or outliers as the mean is sensitive to extreme values.

Chi-Square ($χ^2$)

A test of association between two nominal variables that shows relationships but not causation.

t-test

A parametric test used to compare the means of exactly TWO groups.

ANOVA (Analysis of Variance)

A parametric test used to compare the means of THREE OR MORE groups.

Qualitative Data Analysis

Analysis of non-numerical data capturing experiences, perceptions, emotions, meanings, and social understanding.

Deductive (Top-Down) Approach

Analysis starting with an existing theory or hypothesis and testing it against data.

Inductive (Bottom-Up) Approach

Analysis starting with observations to allow data to guide theory development.

Grounded Theory

A systematic inductive qualitative strategy for building theory directly from collected data.

Coding

Categorising data and assigning labels, properties, meanings, or patterns to words, phrases, or passages.

Thematic Analysis

A six-step process of identifying and interpreting themes: familiarisation, coding, generating themes, reviewing, defining, and writing up.

Discourse Analysis

The study of communication and how language creates effects and communicates power, beliefs, and social meanings.

Mixed Methods Research

Collecting, analysing, and interpreting both quantitative and qualitative data within a single study.

Pragmatism

The philosophy of choosing research methods based on their usefulness for answering a specific research problem.

Explanatory Sequential Design

A mixed methods design where quantitative data is collected first, followed by qualitative data to explain the findings ($QUAN → QUAL$).

Exploratory Sequential Design

A mixed methods design starting with qualitative exploration followed by quantitative empirical testing ($QUAL → QUAN$).

Triangulation

The practice of cross-checking findings using multiple methods or datasets to justify mixed methods research.

Multi-method Research

Using two or more methods from the SAME research strategy or paradigm, distinct from mixed methods.

Secondary Data

Already collected data that is reused by researchers, such as census data, crime statistics, or records.

Big Data (3 Vs)

Data described by three factors: Volume (amount), Velocity (speed), and Variety (different data types).