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Categories

Psychology

Quantitative Psychology

Advanced Quantitative Methods

Basic Concepts in Measurement and Scaling

Basics of Factor Analysis

Classical Test Theory Principles

Cognitive Biases and Heuristics

Common Ethical Considerations in Research

Common Fallacies in Reasoning

Common Psychological Scales and Inventories

Data Visualization Techniques

Descriptive Statistics Basics

Factors Influencing Psychological Measurement

Ethics in Psychological Testing

Experimental Controls in Psychological Research

Famous Psychologists & Their Contributions

Introduction to Item Response Theory

Major Tests and Assessments in Psychology

Learning Theories and Models

Multivariate Statistics Overview

Psychometric Properties

Psychological Measurement Scales

Psychological Constructs

Quantitative Analysis Techniques

Types of Data in Quantitative Research

Types of Psychological Variables

Types of Sampling in Research

Validity in Psychological Testing

Reliability in Psychological Testing

Regression Analysis Concepts

Research Design Types in Psychology

Standardization of Psychological Tests

Types of Data in Quantitative Research

Flashcards

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Nominal Data

Characteristics: Categorical, no inherent order, labelled. Examples: Gender, Race, Marital Status.

Ordinal Data

Characteristics: Categorical, ordered, intervals between values are not consistent. Examples: Socioeconomic status, Education level, Likert scale responses.

Interval Data

Characteristics: Continuous, ordered, equal intervals between values, no true zero. Examples: Temperature in Celsius, IQ scores, Dates.

Ratio Data

Characteristics: Continuous, ordered, equal intervals, true zero. Examples: Age, Income, Distance.

Discrete Data

Characteristics: Countable, finite number of values, gaps between values. Examples: Number of children, Cars sold, Test questions correct.

Continuous Data

Characteristics: Measurable, infinite number of values, no gaps between values. Examples: Height, Weight, Time.

Binary Data

Characteristics: Dichotomous, only two possible values. Examples: Yes/No, True/False, On/Off.

Quantitative Data

Characteristics: Numerical measurements, represents quantity, suitable for arithmetic operations. Examples: Test scores, Blood pressure readings, Survey scores.

Categorical Data

Characteristics: Non-numerical categories, describes attributes or qualities. Examples: Blood type, Vehicle make, Hair color.

Time Series Data

Characteristics: Sequential, data points collected or recorded at successive points in time. Examples: Monthly sales, Daily temperature, Yearly GDP.

Cross-Sectional Data

Characteristics: Collected from multiple subjects at a single point in time or over a short period. Examples: Census data, A survey conducted in a single day, Market research data.

Primary Data

Characteristics: Original, collected first-hand by a researcher for a specific research purpose. Examples: Surveys, Experiments, Observations.

Secondary Data

Characteristics: Sourced from existing data collected for another purpose, secondary analysis. Examples: Government statistics, Academic articles, Historical records.

Univariate Data

Characteristics: One variable, describes a single characteristic, simple analysis. Examples: Height of students, Prices of houses, Age of respondents.

Multivariate Data

Characteristics: Multiple variables analyzed together, explores relationships, complex analysis. Examples: Consumer preferences with demographic variables, Weather patterns with multiple atmospheric variables, Financial data involving several economic indicators.

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