Bandingkan kaedah

	Matriks Kaedah Campuran Dominan Kualitatif	Reka Bentuk Kaedah Campuran Urutan Penerokaan yang Dominan Kualitatif
Bidang	Reka Bentuk Penyelidikan	Reka Bentuk Penyelidikan
Keluarga	Process / pipeline	Process / pipeline
Tahun asal	2003–2009	2003–2007
Pengasas	Charles Teddlie & Abbas Tashakkori (matrix framework); qualitative-dominant weighting drawn from Jennifer Greene and David Morgan	Creswell & Plano Clark; Morse (priority notation)
Jenis	Mixed methods research design variant	Mixed methods research design
Sumber perintis	Teddlie, C., & Tashakkori, A. (2009). Foundations of Mixed Methods Research: Integrating Quantitative and Qualitative Approaches in the Social and Behavioral Sciences. Sage. ISBN: 978-0761930129	Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379
Alias	QUAL-dominant MMM, qualitative-priority mixed methods matrix, QUAL-weighted matrix design, qual-dominant typology matrix	QUAL-dominant exploratory sequential design, qual-first exploratory mixed methods, qualitative-priority exploratory sequential MMR, QUAL → quan exploratory design
Berkaitan	5	6
Ringkasan	The qualitative-dominant mixed methods matrix is a design variant in which the researcher selects and positions a specific mixed methods design within a typological matrix — organized by timing (sequential vs. concurrent) and paradigm weighting — while assigning greater priority to the qualitative strand. Quantitative data play a supporting, supplementary role, and the final inferences are grounded primarily in qualitative findings.	This design begins with a substantive qualitative phase (QUAL) that drives the study, followed by a smaller quantitative phase (quan) used to test, refine, or extend qualitative findings to a broader sample. The qualitative strand holds priority in both scope and interpretation; the quantitative strand serves a confirmatory or generalisability function. It is particularly well suited when theory or instrument development must be grounded in participants' own frameworks before statistical testing.