Marketing Research

Name: Marketing Research
Author: Mike Nguyen

A Technical and Academic Treatment

A technical and academic treatment of marketing research: constructs, substantive domains, and the methods that connect them, with reproducible R code throughout.

Author

Mike Nguyen

Preface

This book is a technical and academic treatment of marketing research. It is written for two readers at once: the doctoral researcher, who wants formal constructs, derivations, identification assumptions, and citations to the primary literature; and the senior technical practitioner, who wants the intuition, the trade-offs, and runnable code. Wherever the two pull in different directions, the book leads with intuition and follows immediately with the formalism, so that neither reader is short-changed.

Three commitments shape every chapter. First, rigor: constructs are defined formally, methods are stated with their estimators and assumptions, and empirical claims are tied to the primary literature in the top marketing journals. Second, reproducibility: quantitative ideas are accompanied by code that runs. Third, verifiability: every cited work carries a DOI, so any claim can be traced to its source.

The book is organized into parts that move from what marketing studies (constructs and substantive domains) to how it studies them (methodology), and then to the advanced seminars and research craft that define the modern field. A short history of marketing thought (see 2 A Short History of Marketing Thought) situates the whole enterprise before the substantive material begins.

No formal prerequisites are required, though a reader comfortable with introductory probability, regression, and a scripting language such as R will get the most from the methodological chapters.

Use this book in your AI assistant

This book is indexed by the Research Methods Library, a Model Context Protocol (MCP) server that makes it available to AI assistants. Given a research question, an assistant connected to the server recommends candidate study designs, states their identifying assumptions, returns runnable R and Python code, and cites the sections of this book and of the author’s other works on which each answer draws. Access is free and requires no account or API key.

Endpoint: https://research-methods-mcp.vercel.app/mcp

Claude (web, desktop, and mobile): Settings → Connectors → Add custom connector; enter the endpoint.
Claude Code: claude mcp add --transport http research-methods https://research-methods-mcp.vercel.app/mcp
ChatGPT: Settings → enable developer mode → add a custom connector of type MCP server with the endpoint and no authentication.
Cursor and Visual Studio Code: Add to Cursor or Install in VS Code.

Configuration for other MCP clients is described on the server’s website.

# Preface {.unnumbered} ```{r} #| label: setup #| include: false # Build a bib database for any R packages used across the book. knitr::write_bib( c(.packages(), "bookdown", "knitr", "rmarkdown"), "packages.bib" ) ``` This book is a technical and academic treatment of marketing research. It is written for two readers at once: the **doctoral researcher**, who wants formal constructs, derivations, identification assumptions, and citations to the primary literature; and the **senior technical practitioner**, who wants the intuition, the trade-offs, and runnable code. Wherever the two pull in different directions, the book leads with intuition and follows immediately with the formalism, so that neither reader is short-changed. Three commitments shape every chapter. First, **rigor**: constructs are defined formally, methods are stated with their estimators and assumptions, and empirical claims are tied to the primary literature in the top marketing journals. Second, **reproducibility**: quantitative ideas are accompanied by code that runs. Third, **verifiability**: every cited work carries a DOI, so any claim can be traced to its source. The book is organized into parts that move from *what* marketing studies (constructs and substantive domains) to *how* it studies them (methodology), and then to the advanced seminars and research craft that define the modern field. A short history of marketing thought (see @sec-history) situates the whole enterprise before the substantive material begins. No formal prerequisites are required, though a reader comfortable with introductory probability, regression, and a scripting language such as R will get the most from the methodological chapters. ::: {.content-visible when-format="html"} ## Use this book in your AI assistant {.unnumbered} This book is indexed by the [Research Methods Library](https://research-methods-mcp.vercel.app/), a Model Context Protocol (MCP) server that makes it available to AI assistants. Given a research question, an assistant connected to the server recommends candidate study designs, states their identifying assumptions, returns runnable R and Python code, and cites the sections of this book and of the author's other works on which each answer draws. Access is free and requires no account or API key. **Endpoint:** `https://research-methods-mcp.vercel.app/mcp` - **Claude** (web, desktop, and mobile): Settings → Connectors → **Add custom connector**; enter the endpoint. - **Claude Code:** `claude mcp add --transport http research-methods https://research-methods-mcp.vercel.app/mcp` - **ChatGPT:** Settings → enable developer mode → add a custom connector of type MCP server with the endpoint and no authentication. - **Cursor** and **Visual Studio Code:** [Add to Cursor](https://cursor.com/en/install-mcp?name=research-methods&config=eyJ1cmwiOiJodHRwczovL3Jlc2VhcmNoLW1ldGhvZHMtbWNwLnZlcmNlbC5hcHAvbWNwIn0%3D) or [Install in VS Code](https://insiders.vscode.dev/redirect/mcp/install?name=research-methods&config=%7B%22type%22%3A%22http%22%2C%22url%22%3A%22https%3A%2F%2Fresearch-methods-mcp.vercel.app%2Fmcp%22%7D). Configuration for other MCP clients is described on the [server's website](https://research-methods-mcp.vercel.app/). :::