Mixed methods research

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Mixed Methods Research - Best of Both Worlds

  • Integrates quantitative (numerical) and qualitative (thematic) data to provide a more comprehensive understanding of a research problem than either approach alone.
  • Rationale: Triangulation of data, deeper insights, and corroboration of findings.

Mixed Methods Research Designs

High-Yield: A common application is using qualitative interviews to explain unexpected statistical findings from a quantitative survey (Explanatory Sequential Design).

Core Designs - The Research Recipes

  • Combines quantitative (QUAN) and qualitative (QUAL) data collection to provide a comprehensive understanding. The timing, priority, and mixing of data define the core designs.
  • Convergent Design: QUAN and QUAL data are collected at the same time. The results are then merged to see if the findings corroborate or contradict each other.
  • Explanatory Sequential Design: Quantitative data is collected first. The subsequent qualitative phase is used to explain or elaborate on the initial quantitative results.
  • Exploratory Sequential Design: Begins with qualitative data to explore a topic. Findings then inform the development of a quantitative instrument or phase.

Notation reveals the story: Capitalization indicates the dominant method (e.g., QUAN → qual). The arrow → implies a sequential order, while a + sign indicates concurrent data collection.

Data Integration - Putting Pieces Together

  • Core Goal: To combine quantitative (QUAN) and qualitative (QUAL) data to create a more comprehensive understanding than either could alone.
  • Integration occurs at various stages: design, methods, interpretation, and reporting.

Integration Strategies & Timing

StrategyTiming & SequencePurpose
ConvergentQUAN + QUAL (Concurrent)Compare/contrast findings; triangulation.
ExplanatoryQUAN → qualUse QUAL data to explain surprising or outlier QUAN results.
ExploratoryQUAL → quanUse QUAL findings to build an instrument or theory to be tested by QUAN data.

Basic Mixed Methods Research Designs

Integration Flow

Strengths & Limitations - The Give and Take

  • Strengths:

    • Comprehensive: Integrates qualitative depth with quantitative breadth.
    • Triangulation: Cross-verifies findings, increasing study validity.
    • Flexibility: Adapts to complex research questions.
  • Limitations:

    • Complex: Difficult design and data integration.
    • Resource-intensive: High cost and time commitment.
    • Contradictory findings can be hard to reconcile.

High-Yield: Triangulation uses one method to validate or expand on the other, significantly boosting study credibility and providing a more complete picture.

  • Mixed methods research integrates quantitative (numerical) and qualitative (thematic) data for a holistic understanding.
  • Its core strength is triangulation, using one data type to validate or expand on the findings of the other.
  • In explanatory sequential design (QUAN → qual), quantitative data is collected first, followed by qualitative data to explain the results.
  • In exploratory sequential design (QUAL → quan), qualitative data is gathered first to develop a hypothesis for later quantitative testing.
  • Convergent design involves the simultaneous collection of both data types, which are then merged during analysis.

Practice Questions: Mixed methods research

Test your understanding with these related questions

A study is funded by the tobacco industry to examine the association between smoking and lung cancer. They design a study with a prospective cohort of 1,000 smokers between the ages of 20-30. The length of the study is five years. After the study period ends, they conclude that there is no relationship between smoking and lung cancer. Which of the following study features is the most likely reason for the failure of the study to note an association between tobacco use and cancer?

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Flashcards: Mixed methods research

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_____ is used to model the linear relationship between a dependent variable and an independent variable

TAP TO REVEAL ANSWER

_____ is used to model the linear relationship between a dependent variable and an independent variable

Linear regression

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