#integration

Design APIs that drive product adoption and ecosystem growth. API strategy considerations: 1. Business model: free vs. paid, usage-based pricing. 2. Target audience: internal teams, partners, public developers. 3. Scope: core features vs. comprehensive platform. 4. Versioning: backwards compatibility, deprecation policy. Developer experience (DX): 1. Documentation: clear examples, interactive API explorer. 2. Authentication: simple setup, secure token management. 3. Error handling: descriptive error messages, status codes. 4. Rate limiting: fair usage policies, graceful degradation. 5. SDKs: libraries for popular programming languages. API design principles: 1. RESTful design: consistent resource naming, HTTP methods. 2. Idempotency: safe to retry operations. 3. Pagination: handle large datasets efficiently. 4. Filtering: query parameters for data selection. Developer support: 1. Community forum: peer-to-peer help. 2. Technical support: dedicated developer relations team. 3. Sandbox environment: test without production impact. Success metrics: API adoption, developer retention, applications built, revenue attribution.

Combine qualitative and quantitative data meaningfully. Integration approaches: 1. Convergent parallel: collect QUAL+QUAN simultaneously, analyze separately, merge findings in interpretation. 2. Explanatory sequential: QUAN → qual, quantitative first then qualitative to explain results. 3. Exploratory sequential: qual → QUAN, qualitative first to develop instruments or hypotheses. 4. Embedded: one method secondary, embedded within larger study. Integration techniques: 1. Data transformation: quantitize qualitative data for statistical analysis. 2. Joint displays: side-by-side comparison tables showing confirmatory, contradictory, and expansive findings. 3. Meta-inferences: draw conclusions that synthesize both types of data. Quality criteria: use both quantitative validity/reliability and qualitative trustworthiness standards. Document rationale for mixed methods approach clearly.