Prof. Dr. Sugiyono : Metode Sequential Exlpanatory dalam Mixed Methods-Eps.2 @Suyantoid

Summary

In this video, Professor Sugiono from Universitas Negeri Yogyakarta discusses the sequential explanatory mixed-method research design. The focus is on combining quantitative and qualitative methods, starting with a quantitative phase that includes hypothesis testing and data analysis, followed by a qualitative phase to enrich and validate the findings. The process covers the development of research questions, sampling techniques (random for quantitative and purposive or snowball for qualitative), and the potential for new discoveries through mixed methods. The session aims to guide students in crafting comprehensive research proposals and understanding the integration of data types for robust conclusions.

Takeaways

• 😀 The channel 'suyanto.id' shares knowledge and lifelong learning, with creative editing from Mas Heri, a teacher at SMP Negeri 2 Karanganyar, Ngawi.
• 😀 In this episode, Professor Sugiono, a renowned academic from Universitas Negeri Yogyakarta, discusses research design, specifically focusing on sequential explanatory mixed methods.
• 😀 Sequential explanatory design involves using quantitative methods first, followed by qualitative methods to strengthen and expand the findings.
• 😀 The quantitative part of the study includes problem identification, hypothesis testing, and data analysis through random sampling using tools like surveys or tests.
• 😀 The qualitative part uses purposive or snowball sampling, analyzing data through interviews, observations, or documentation, and complements the quantitative results.
• 😀 A key feature of sequential explanatory research is that qualitative data provides context and insight that quantitative data cannot, such as emotional responses or body language.
• 😀 Research problems in sequential explanatory designs are typically divided into two parts: quantitative questions and qualitative follow-ups. Combining both leads to a fuller understanding of the issue.
• 😀 A sample research title was suggested: 'Competency of Lecturers and Student Learning Outcomes at Indraloka University' with a focus on quantitative (competency and learning outcomes) and qualitative (contextual insights) data.
• 😀 Researchers must carefully formulate research questions that align with both the quantitative and qualitative aspects of the study. For instance, first addressing descriptive, then associative questions.
• 😀 One potential outcome of using mixed methods is that the quantitative and qualitative findings can either complement each other or reveal new insights that were not anticipated at the start of the research.
• 😀 Researchers must be cautious of conflicting results between the two methods, as discrepancies could indicate areas requiring further investigation or adjustment in the research framework.

Q & A

What is the core concept of sequential explanatory research design?

Sequential explanatory research design is a mixed-methods approach that starts with quantitative research followed by qualitative research. This design allows the quantitative findings to be further explored and explained using qualitative data.

What are the two primary types of sequential explanatory designs discussed in the video?

The two primary types are 'Explanatory Sequential,' where the quantitative phase comes first, followed by the qualitative phase, and 'Exploratory Sequential,' where qualitative research comes before the quantitative phase.

How does quantitative research contribute to the sequential explanatory design?

Quantitative research in sequential explanatory design helps establish statistical relationships between variables, test hypotheses, and provide initial data that forms the basis for the subsequent qualitative phase.

Why is qualitative research added after the quantitative phase in sequential explanatory design?

Qualitative research is added to enrich and deepen the understanding of the quantitative results. It helps explain underlying factors, human behaviors, or emotions that quantitative data alone cannot capture.

What is the purpose of data triangulation in mixed-methods research?

Data triangulation involves combining qualitative and quantitative data to confirm, validate, or expand upon the findings. This approach provides a more comprehensive understanding of the research problem by using different data sources.

What are the key differences in sampling methods between the quantitative and qualitative phases?

In the quantitative phase, sampling is typically random, aiming to represent the larger population. In the qualitative phase, purposive or snowball sampling is used to select specific individuals who can provide in-depth insights.

How are research problems formulated in a sequential explanatory design?

In sequential explanatory design, research problems should be framed to address both quantitative and qualitative aspects. Initially, quantitative questions are posed, followed by qualitative questions that explore the deeper context or implications of the quantitative findings.

What is the significance of using both descriptive and associative questions in the research design?

Descriptive questions help gather information about the current state of the variables, while associative questions examine relationships or influences between variables. These are necessary for understanding both the individual components and their connections.

What does the researcher need to be cautious about when combining quantitative and qualitative data?

Researchers must be careful when interpreting conflicting findings between quantitative and qualitative data. They need to ensure the findings are aligned or can be reasonably explained, as contradictions may undermine the validity of the study.

How does the combination of quantitative and qualitative data potentially lead to new discoveries?

The combination of data from both methods can uncover new variables or factors that were not anticipated at the start of the research. Qualitative data, in particular, might reveal insights that quantitative data alone cannot provide.