Research Design & Methods

The chapter of research design and methods, as it can be found in the middle of the research paper, is the heart of your research. Or engine. Or whatsoever.

It bridges over the theory and practice parts, so it links your formerly stated research problem to its upcoming solution. It justifies whether your research is problem- or paradigm-driven regarding its emergence and plans or designs the empirics along the following steps:

• 1. Introduction: Stating the research objective, questions
• 2. Design
  • Problem- or paradigm-driven
  • Planning of data collection and context
• 3. Data collection and selection
• 4. Generation or elaboration of data

Methods: Situating the chosen methods within or without the tradition of the field of study

• 5. Validity, Reliability, Objectivity, Generalizability
  • Design to achieve those through data collection and methodology
  • Justification for subject or data selection and sampling procedure
• 6. Potential limitations/Threats

Research Objective & Questions

Research Objective: Six important guidelines for the research objective:

• 1. Briefly and concisely presented
• 2. Presented in a logical sequence
• 3. Realistic: e.g. achieved within the expected timeframe and available resources
• 4. Uses action verbs that are specific enough to be evaluated or measured: e.g. assess, determine, compare, verify, calculate, describe
• 5. Static once the study work begins: i.e. objectives should not be moving targets

Research Questions

• The fundamental core of a research
• Support the research objective

In hand with the research objective they:

• Focuse on the study
• Determine the methodology
• Guide all stages of inquiry, analysis, and reporting

Characteristics of a good research question:

• Feasible
• Clear
• Significant
• Ethical

Data Collection and (Analysis) Methods

Ask the next questions from yourself before continuing:

• Primary or secondary data?
• Qualitative or quantitative methods?
• Methods or just data interpretation?
• Data generation or analysis?
• Where is the added-value?

Data Collection

Primary: generation of dataset

• Experiment and observation e.g. participant observation
• Surveys and questionnaires
• Interviews: structured, semi-structured, non-structured
• Justify and eliminate the possible bias

Secondary: elaboration of dataset from existing database

• Justify why from the chosen database
• Apply a level of reliability and objectivity to the database

Qualitative Methods

Design:

• Naturalistic
• Emergent: new path
• Purposeful: insight

Data Collection:

• Data: “thick description”
• Personal experience and engagement
• Empathic neutrality
• Dynamic systems: flexibility for on-going processes

Analysis:

• Unique case orientation
• Unstructured data
• Inductive analysis
• Holistic perspective: complex systems
• Context sensitive: settings
• Voice, perspective, and reflexivity

Limitations:

• Validity: drifting away
• Reliability: difficult replication
• Objectivity:  participant observation and engagement
• Generalizability: small samples

 Quantitative Methods

Design:

• All aspects of the study are carefully designed before data is collected
• Clearly defined research question

Data Collection:

• Structured data
• Numbers and statistics
• Questionnaires or computer software
• Construct statistical models

Analysis:

• Validity: focused on research objective
• Reliability: results may be replicated or repeated
• Objectivity: personal bias can be avoided
• Generalizability: large samples

Limitations:

• Miss contextual detail
• Static and rigid approach
• Statistically significant but humanly insignificant
• Less detail on behavior, attitudes, and motivation

Photo: https://www.slideshare.net/JITHINKT/research-design-67446427 Retrieved: 19/04/2018

Editor A.S., 2018