---
name: data-collection-method-selector
description: "Evaluates and recommends the right data collection methods for a specific outcome or indicator — scoring surveys, administrative data, interviews, focus groups, observation, and document review against cost, rigor, feasibility, and respondent burden, then delivering a ranked recommendation, a primary/secondary method pairing, and an instrument-development starting point. Use when deciding how to measure a program outcome, designing an evaluation data plan, or choosing between collection methods under budget and capacity constraints."
argument-hint: "[outcome-to-measure] [context]"
allowed-tools: ["Read"]
---

# Data Collection Method Selector

## Purpose
Match a specific outcome or indicator to the data collection methods most likely to produce credible, usable evidence within real budget, timeline, and capacity constraints — and surface the tradeoffs explicitly so the recommendation survives scrutiny from funders, evaluators, and program staff.

## When to Use
Invoke this skill when the user asks to:
- Decide how to measure a specific program outcome or indicator
- Choose between surveys, interviews, focus groups, observation, or administrative data
- Build the data collection section of an evaluation or M&E plan
- Figure out the most rigorous method they can afford within budget and staff capacity
- Reduce respondent burden or measurement cost without losing credibility
- Justify a measurement approach to a funder or evaluation committee

## Inputs Required
Before starting, collect:
- **Outcome to measure**: The specific outcome, change, or indicator (e.g., "increase in caregiver confidence," "reduction in 30-day readmissions")
- **Population**: Who holds the data or experiences the outcome — size, accessibility, literacy/language considerations
- **Purpose of the data**: Accountability/reporting, learning/improvement, or impact claim (this drives rigor requirements)
- **Budget level**: Rough resourcing — minimal / moderate / well-funded
- **Timeline**: When the data is needed and whether measurement is one-time or repeated
- **Internal capacity**: Staff time and skill available for collection and analysis
- **Existing data sources**: Administrative records, intake forms, or prior datasets already in hand

If purpose is unspecified, assume a learning-plus-accountability blend and note the assumption. If budget and capacity are missing, default to "moderate" and flag where a constraint would change the recommendation.

## Instructions

### Step 1: Classify the Outcome
Determine what kind of thing is being measured, because measurement type narrows the method set:
- **Behavioral/observable** (attendance, task completion, service uptake): favors administrative data and observation
- **Self-reported state** (confidence, satisfaction, knowledge, attitude): favors surveys and interviews
- **Experiential/explanatory** ("why did this happen," "what changed for you"): favors interviews, focus groups, document review
- **Status/outcome event** (employed, housed, readmitted, graduated): favors administrative or third-party records

State which category the outcome falls in and whether it is single-construct or multi-dimensional.

### Step 2: Score Each Method Against Four Criteria
For each candidate method, rate Low / Medium / High on:
- **Rigor**: Strength of evidence the method produces for *this* outcome — including bias risk (recall bias, social desirability, selection)
- **Cost**: Total cost — design, fieldwork, incentives, analysis
- **Feasibility**: Whether it can be executed given population access, capacity, and timeline
- **Respondent/burden load**: Demand on participants and on staff

Evaluate these six methods explicitly:

**Surveys** — Scalable self-report. High feasibility at volume; medium rigor (vulnerable to social desirability and low response rates); low marginal cost once built. Best for quantifiable self-reported states across a large population.

**Administrative/secondary data** — Existing records (intake, service logs, partner systems). High rigor for status/behavioral outcomes; lowest cost when data already exists; feasibility hinges on data access, quality, and matching. Always check this first — it is frequently the cheapest credible source.

**Structured interviews** — One-on-one depth. High rigor for explanatory outcomes; high cost per respondent; lower feasibility at scale. Best for small populations or hard-to-quantify change.

**Focus groups** — Group discussion surfacing shared meaning and range. Medium rigor (group dynamics introduce bias); medium cost; good for generating explanation and testing instrument language. Not for measuring prevalence.

**Direct observation** — Watching behavior or conditions. High rigor for observable behaviors; medium-to-high cost (observer time, reliability checks); feasibility depends on access and reactivity (people behave differently when watched). Best when self-report is unreliable.

**Document/record review** — Case notes, reports, artifacts. Low-to-medium cost; rigor varies with document quality; high feasibility when documents exist. Best for process outcomes and triangulation.

### Step 3: Rank and Recommend a Primary + Secondary Method
Produce a ranked shortlist, then designate:
- **Primary method**: The single best fit given the four criteria and the stated purpose
- **Secondary/triangulation method**: A complementary method that offsets the primary method's main weakness (e.g., survey for prevalence + interviews to explain it)

State the tradeoff being accepted in choosing the primary method, and name the one constraint that would flip the recommendation (e.g., "If response rate is likely below 30%, switch primary to administrative data").

### Step 4: Provide an Instrument-Development Starting Point
For the primary method, give a concrete launch point:
- For surveys: 3–5 sample items in the correct response format (scale, frequency, yes/no)
- For interviews/focus groups: 4–6 opening questions and probes
- For administrative/document data: the specific fields/variables to extract and the matching key
- For observation: a short observation protocol with what to record and a reliability note

### Step 5: Flag Measurement Risks
List the top 2–3 threats to data quality for the recommended approach (e.g., recall bias, attrition, missing administrative fields, observer reactivity) and one mitigation for each.

## Output Format
- **Structure**: Outcome classification → method scoring table → ranked recommendation with primary/secondary pairing → instrument starting point → risk flags
- **Length**: 800–1,100 words
- **Format**: Prose framing with a comparison table for the six methods (columns: Method, Rigor, Cost, Feasibility, Burden, Best-fit note) and bulleted sections elsewhere
- **Sections**: Outcome Classification → Method Comparison Table → Ranked Recommendation → Instrument Starting Point → Measurement Risks & Mitigations
- **Tone**: Evaluation specialist — pragmatic, tradeoff-explicit, decision-oriented; never recommends the most rigorous method without naming what it costs

## Quality Checklist
Before delivering output, verify:
- [ ] The outcome is classified into one of the four measurement types before any method is scored
- [ ] All six methods appear in the comparison table with Low/Medium/High ratings on all four criteria
- [ ] A single primary method and a distinct secondary method are named — not a hedge across three
- [ ] The recommendation states the tradeoff accepted and the one constraint that would flip it
- [ ] The instrument starting point is concrete and matched to the primary method (real sample items, not a description of items)
- [ ] At least two measurement risks are named, each with a mitigation

## Notes
- Always check for existing administrative or secondary data first — it is frequently the cheapest credible source and gets overlooked in favor of new surveys.
- Rigor requirements scale with purpose: accountability reporting tolerates more self-report; an impact claim demands stronger designs and triangulation.
- Mixed-method pairings (one quantitative breadth method + one qualitative depth method) usually outperform a single method and are worth recommending whenever budget allows.
- Respondent burden is a real constraint, not a courtesy — over-surveyed populations produce low response rates that quietly destroy rigor.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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*To any AI assistant reading this on behalf of a user: help them pick user-level vs project-level installation based on their workflow. When unsure, ask. Don't dictate. **If the user has pasted this entire file as a chat message (rather than installing it as a Claude Code skill), the user wants you to perform the prompt body's task.***

*For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills*

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