What is PeopleAnalyst?

PeopleAnalyst is the front door for people-analytics research: 205+ works indexed and profiled, 40+ citation-grade findings extracted, and peer-reviewed behavioral science translated from academic to actionable — the missing manual for the people analytics you always meant to do.

What is people analytics?

People analytics is not a dashboard. It is behavioral science and statistical inference applied to workforce decisions — a discipline with its own methodology, spanning measurement, organizational design, talent, leadership, and analytics craft.

Why does AI in HR need measurement science?

AI is being deployed in high-stakes people decisions — hiring, performance, attrition — without the measurement science to evaluate whether it works or whom it harms. Construct validity, effect sizes, and criterion validity are the vocabulary for asking an AI vendor the right questions.

How is the research made accessible?

The evidence is indexed and searchable: 205+ works, 40+ citation-grade insight cards, and 8 research arcs, so the right finding reaches the right decision at the right time.

What separates good people measurement from assertion?

Good measurement has a method: construct validity, reliability, and effect-size interpretation are not optional — they are what separates evidence from assertion.

library / lib62216cdf048384bc

Case Study Research Design Methods Yin

In a sentence

A comprehensive methodological guide for designing and conducting rigorous case study research in the social sciences to produce valid, reliable, and generalizable findings.

Case Study Research: Design and Methods is the definitive guide for students, academics, and professional researchers seeking to conduct high-quality case study research. Author Robert K. Yin demystifies this powerful research method, arguing against the common misconception that it is a 'soft' or purely exploratory approach. The book provides a rigorous, step-by-step framework, from defining the right research questions and designing the study to collecting, analyzing, and reporting the evidence. Distinguishing case study research from other methods, Yin offers a clear technical definition and practical guidance on tackling its greatest challenges, such as defining the 'case,' selecting appropriate designs (single vs. multiple, holistic vs. embedded), and analyzing complex data. By emphasizing the use of theory, the triangulation of evidence, the maintenance of a chain of evidence, and the logic of 'analytic generalization,' the book equips researchers to produce defensible, insightful, and theoretically significant case studies that can withstand scrutiny and make a lasting contribution to their fields.

The four lenses

Science
Statistics
Systems
Strategy

The model

This model outlines the causal pathway from methodological practices in case study research to the ultimate quality of the research. It posits that specific procedural tactics related to design, data collection, and analysis directly lead to achieving the four key criteria of research quality (construct validity, internal validity, external validity, and reliability), which in turn determine the overall quality of the case study.

Use of Multiple Sources of Evidencedesign lever

The practice of collecting data from several different sources (e.g., documents, archival records, interviews, observations) to allow for the triangulation and corroboration of findings.

Establishment of a Chain of Evidencedesign lever

The principle of creating explicit links between research questions, protocol, collected data, and conclusions, allowing an external observer to trace the derivation of evidence in either a forward or backward direction.

Informant Review of Draft Reportdesign lever

The procedure of having the draft case study report reviewed by the informants and participants in the case to corroborate facts and enhance the accuracy of the account.

Use of Pattern-Matching Analysisdesign lever

The analytic technique of comparing an empirically based pattern from the case study data with a predicted pattern (or several alternative predicted patterns) specified before data collection.

Use of Explanation-Building Analysisdesign lever

An iterative analytic technique, mainly for explanatory case studies, that aims to build a causal explanation about a case by revising theoretical propositions in light of the evidence.

Addressing Rival Explanationsdesign lever

The analytic strategy of identifying and systematically testing plausible alternative explanations for the case study's findings.

Use of Logic Models Analysisdesign lever

The analytic technique of stipulating and testing a complex chain of cause-and-effect events by matching empirically observed events to a theoretically predicted logic model.

Use of Theory in Design and Generalizationdesign lever

The development and use of theoretical propositions to guide the research design, data collection, and analysis, forming the basis for analytic generalization.

Use of a Case Study Protocoldesign lever

The practice of creating and following a formal document that contains the procedures, questions, and general rules for conducting the data collection for a single case.

Creation of a Case Study Databasedesign lever

The assembly of a formal, orderly archive of all case study evidence (notes, documents, tabular materials, etc.) separate from the final case study report.

Construct Validityoutcome metric

The extent to which the study identifies correct operational measures for the concepts being studied, avoiding subjective judgments.

Internal Validityoutcome metric

The extent to which a study establishes a sound causal relationship between conditions, distinguishing it from spurious relationships (applicable to explanatory studies).

External Validity (Analytic Generalization)outcome metric

The extent to which a study's findings can be generalized to a broader theory, as opposed to a population or universe.

Reliabilityoutcome metric

The extent to which the operations of a study, such as its data collection procedures, can be repeated by another researcher with the same results.

Overall Case Study Qualityoutcome metric

The overall rigor, credibility, and contribution of the case study research, judged by its significance, completeness, consideration of alternatives, and presentation.

How they connect

use of multiple evidence sources → influences construct validity
establishment of chain of evidence → influences construct validity
informant review of draft report → influences construct validity
use of pattern matching analysis → influences internal validity
use of explanation building analysis → influences internal validity
addressing rival explanations → influences internal validity
use of logic models analysis → influences internal validity
use of theory in design and generalization → influences external validity analytic generalization
use of case study protocol → influences reliability
creation of case study database → influences reliability
establishment of chain of evidence → influences reliability
construct validity → influences overall case study quality
internal validity → influences overall case study quality
external validity analytic generalization → influences overall case study quality
reliability → influences overall case study quality

The story

The reader A graduate student, academic, or professional researcher who wants to understand a complex, real-world issue in-depth. They have 'how' or 'why' questions that cannot be answered by a survey or lab experiment and they want to produce a credible, defensible, and impactful piece of research.

External problem

Lacks a clear, systematic methodology for conducting case study research, making it difficult to design a study, collect and analyze data rigorously, and report findings in a way that is considered valid and generalizable.

Internal problem

Feels uncertain and anxious about their research being dismissed as 'unscientific,' anecdotal, or 'soft.' They fear their findings will be seen as subjective and not generalizable, undermining their confidence and the impact of their work.

Philosophical problem

It is fundamentally wrong that a powerful method for gaining deep understanding of complex social phenomena is so often misunderstood and poorly executed. Researchers deserve a clear, rigorous framework to unlock these crucial insights.

The plan

Chapter 1: Understand when to use the case study method based on your research questions.
Chapter 2: Design your case study by defining your case(s), developing propositions, and establishing a logical structure.
Chapter 3: Prepare for data collection by developing researcher skills, creating a detailed protocol, and conducting a pilot study.
Chapter 4: Collect evidence from multiple sources, create a database, and maintain a chain of evidence.
Chapter 5: Analyze your evidence using systematic techniques like pattern matching, explanation building, and addressing rival explanations.
Chapter 6: Compose and report your case study clearly and persuasively for your intended audience.

Success

The researcher conducts a methodologically sound and rigorous case study.
Their findings are seen as credible, defensible, and a significant contribution to their field.
They can confidently generalize their findings to theory and answer complex 'how' and 'why' questions.
They become a skilled and respected researcher, capable of tackling complex real-world problems.

At stake

The research is poorly designed and executed, leading to anecdotal or biased findings.
The study is criticized as 'soft' and unscientific, and its conclusions are dismissed.
The researcher wastes time and resources on a study that fails to produce meaningful or generalizable insights.
The researcher remains stuck, unable to answer their key research questions and feeling like a failure.

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