A topic in the Open Knowledge Graph — a free, open map of 15,290 topics and the order to learn them in.

Job Analysis

Graduate Depth 61 in the knowledge graph ☐ I know this ☆ Set as goal
9topics build on this
277prerequisites beneath it
See this on the map →
Levels of MeasurementThe Scientific Method in Psychology→→Employee RecruitmentHuman Factors and Ergonomics+6 more
job-analysis KSAOs task-analysis work-analysis

Core Idea

Job analysis is the systematic process of collecting information about a job's tasks, responsibilities, required knowledge, skills, abilities, and other characteristics (KSAOs). It serves as the foundation for virtually every I-O psychology function — from personnel selection and performance appraisal to training design and compensation. Without a rigorous job analysis, organizations risk building HR systems that are legally indefensible and practically ineffective because they are not grounded in what the job actually requires.

Explainer

Job analysis is where I-O psychology meets the actual work. Before you can select the right people, train them effectively, or evaluate their performance fairly, you need to know what the job is — not in vague terms, but with the specificity required to build measurement systems around it. Job analysis provides that specificity by systematically documenting what workers do and what they need to know and be able to do.

The field distinguishes two broad approaches. Task-oriented job analysis focuses on the work itself: what tasks are performed, how frequently, how critically, and under what conditions. Functional Job Analysis (FJA), developed by Sidney Fine, is a classic task-oriented method that rates each task on its complexity with respect to data, people, and things. Worker-oriented job analysis, by contrast, focuses on the human attributes required: cognitive abilities, physical capabilities, personality characteristics, and specialized knowledge. The Position Analysis Questionnaire (PAQ), developed by McCormick, is the best-known worker-oriented instrument, rating jobs on 194 standardized elements. Most practitioners combine both approaches because selection and training require knowing both what the job demands and what the person needs to bring.

The methods for collecting job analysis data include interviews with incumbents and supervisors, direct observation, structured questionnaires, work diaries, and analysis of existing documentation. Each has tradeoffs. Interviews are flexible and can capture nuance, but incumbents may inflate the importance or difficulty of their work. Observation captures behavior directly but is impractical for jobs with long task cycles or rare critical incidents. Questionnaires enable large-sample data collection but flatten context. Best practice is to triangulate across multiple methods and multiple sources (incumbents, supervisors, sometimes customers) to build a robust picture.

Job analysis carries significant legal weight. In the United States, the Uniform Guidelines on Employee Selection Procedures (1978) require that selection procedures be shown to be job-related and consistent with business necessity. The primary way to establish job-relatedness is through a job analysis that links selection criteria to actual job requirements. Courts have repeatedly ruled against employers whose selection systems lacked a documented job analysis foundation. This legal dimension makes job analysis not just scientifically important but practically indispensable for any organization that wants its HR practices to withstand scrutiny.

A growing challenge is that jobs are changing faster than traditional job analyses can keep up. The rise of team-based work, rapidly evolving technology, and fluid role boundaries has led some researchers to advocate for broader "work analysis" or competency modeling approaches that focus on transferable competencies rather than static task lists. While these newer approaches sacrifice some specificity, they may be more durable in environments where job content shifts frequently. The tension between specificity and durability is an active area of research in the field.

Practice Questions 4 questions

Prerequisite Chain

Understanding Zero → The Number Zero → Counting to Five → Counting to 10 → Counting to 20 → Counting a Set of Objects Up to 20 → Cardinality: The Last Number Counted → Matching Numerals to Quantities → Subitizing Small Quantities → Addition Within 10 → Making 10 as an Addition Strategy → Addition Within 20 → Doubles and Near Doubles → Doubles Facts Within 10 → Near Doubles Facts Within 20 → Mental Math Strategies for Addition → Mental Math: Adding and Subtracting Tens → Addition Within 100 → Repeated Addition as Multiplication → Multiplication as Equal Groups → Multiplication: Arrays → Basic Multiplication Facts Through 10 → Multiplication Facts Within 100 → Division as Equal Sharing → Division as Grouping (Measurement Division) → Division: Grouping (Repeated Subtraction) Model → Division: Fair Sharing Model → Division as Equal Sharing → Division as Grouping → Basic Division Facts → Division Facts Within 100 → Multiplication and Division Fact Families → Relationship Between Multiplication and Division → Division Facts as Inverse of Multiplication → Remainders and Quotients in Division → Division Word Problems → Multi-Step Word Problems → Solving Multi-Step Word Problems → Multiplication Word Problems → Division Word Problems → Introduction to Long Division → Factors and Multiples → Prime and Composite Numbers → Equivalent Fractions → Relating Fractions and Decimals → Decimal Place Value → Integers and the Number Line → Comparing and Ordering Integers → Length Comparison → Measuring Length with Non-Standard Units → Measuring Length With a Ruler → Measuring with Feet and Meters → Estimating Lengths → Line Plots with Measurement Data → Organizing and Representing Data → Creating Tally Charts → Creating and Reading Picture Graphs → Scaled Bar Graphs → Mean, Median, and Mode → Measures of Spread → Levels of Measurement → Job Analysis

Longest path: 62 steps · 277 total prerequisite topics

Prerequisites (2)

Leads To (8)