Untitled Flashcards Set

Quantitative Methods

Test theories using numbers.

Qualitative Methods

Analyse behaviour and language (e.g., observations, conversations).

Research Process

Initial observation → Theory generation → Hypothesis → Data collection → Data analysis.

Categorical Measurement

Divides entities into distinct categories (e.g., cat vs dog).

Continuous Measurement

Produces scores (e.g., amount an animal eats).

Measurement Error

Discrepancy between actual values and represented values.

Non-Experimental Design

Observes natural behaviours without interference.

Experimental Design

Manipulates variables to examine effects on outcomes.

Between Groups Study

Different entities in different experimental conditions.

Within Groups Study

Same entities participate in all conditions.

Mean

Average of values.

Mode

Most frequent value.

Median

Middle value in an ordered set.

Bimodal

Data with two modes.

Multimodal

Data with multiple modes.

Histogram

A frequency distribution graph that visualises normality.

Positive Skew

Scores cluster at the lower end; tail points to high values.

Negative Skew

Scores cluster at the higher end; tail points to low values.

Leptokurtic

Positive kurtosis (peaked distribution).

Platykurtic

Negative kurtosis (flat distribution).

Homoscedasticity

Consistent variance across data.

Heteroscedasticity

Inconsistent variance across data.

Z-Scores

Standardises data (mean = 0, SD = 1).

Null Hypothesis (H₀)

Indicates no effect or no difference exists.

Statistical Significance

Significance levels (p-value) indicate the likelihood of the result occurring by chance.

Correlation

Measures the relationship between two variables.

Linear Relationship

A direct connection between two variables, e.g., anxiety and exam performance.

Spearman’s Rho

A non-parametric test used for ranked data.

Partial Correlation

Controls for a third variable affecting the relationship between two variables.

Effect Size

Measures the strength of the relationship between variables.

Positive Correlation

As one variable increases, the other also increases.

Negative Correlation

As one variable increases, the other decreases.

Independent Samples T-Test

Compares two groups based on independent data.

Paired Samples T-Test

Compares two related groups (e.g., before and after measurement).

One-Way ANOVA

Compares multiple groups based on a single independent variable.

Two-Way ANOVA

Compares multiple groups based on two independent variables.

Bonferroni Correction

Adjusts for multiple comparisons to control Type I error rates.

Omnibus Test

An overall test of differences between groups.

ANCOVA

Extends ANOVA by controlling for additional covariates.

Research Ethics

Guidelines that protect participants' rights and ensure fair practices.

Informed Consent

Ensuring participants are aware of their involvement and risks.

Simple Regression

Predicts the value of one variable based on one predictor variable.

Multiple Regression

Predicts the value of one variable based on two or more predictor variables.

Mixed ANOVA

Combines between-groups and within-groups designs.

WEIRD Bias

Overrepresentation of Western, Educated, Industrialised, Rich, Democratic populations.

Inter-Rater Reliability

Consistency between observers.

Internal Consistency

Consistency across items in a test.

Test-Retest Reliability

Stability of test results over time.

External Validity

Generalisability of results to other populations/settings.

Internal Validity

Confidence in causal inferences from the study.

Construct Validity

Accurate representation of the variables being studied.

Cohen’s d

Effect size measure for differences between two means.

General Linear Model (GLM)

Predicts a dependent variable using multiple independent variables.

Interaction Effects

Combined effect of independent variables on the dependent variable.

Assumption Testing for Regression

Evaluates key assumptions for regression analysis.

Cook’s Distance

Measures the influence of individual data points.