Research Methods and design that you should know
- 1. RESEARCH METHODS AND DESIGN AN OVERVIEW OF APPROACHES TO CONDUCTING RESEARCH GROUP 1
- 2. 2 WHAT ARE RESEARCH METHODS? It provides a clear and logical framework for conducting and evaluating research. It helps researchers to plan, organize, and execute their research in a systematic and consistent manner.
- 3. 3 RESEARCH METHODS COMPONENTS • Research objectives • Data collection methods • Sampling strategy • Data analysis techniques
- 5. 5 QUALITATIVE RESEARCH • Focuses on understanding phenomena through non- numerical data. • Often explores experiences, perceptions, and meanings. • Uses interviews, focus groups, and observations.
- 6. 6 QUALITATIVE RESEARCH METHODS CHARACTERISTICS •Collects descriptive data from interviews, case studies, or ethnography. •Focuses on understanding the context and meaning behind behaviors or phenomena.
- 8. 8 Phenomenology Purpose: To explore and understand lived experiences of individuals and how they make sense of those experiences. Focus: The essence of a phenomenon as experienced by individuals. Method: In-depth interviews, often with open-ended questions to understand participants' perceptions and meanings. Example: Investigating how people with chronic illness experience daily life and coping mechanisms.
- 9. 9 Grounded Theory Purpose: To develop a theory that is grounded in data collected from the field, rather than testing a pre- existing theory. Focus: The generation of theories based on patterns and relationships found in qualitative data. Method: Constant comparison method, coding, and iterative data collection and analysis. Example: Exploring how employees adapt to organizational change and developing a theory of workplace adaptation.
- 10. 10 Ethnography Purpose: To study and describe the culture, behaviors, and social interactions of a group or community. Focus: Understanding the social dynamics of a particular group or community in its natural setting. Method: Participant observation, interviews, and immersion in the group or community for an extended period of time. Example: Studying the daily life and rituals of a specific indigenous tribe or a subculture like a skateboarding community.
- 11. 11 Case Study Purpose: To conduct an in-depth, detailed examination of a single case or a small number of cases within a real-life context. Focus: A thorough exploration of a specific instance or example of a phenomenon. Method: Data collection from multiple sources, such as interviews, documents, and observations. Example: Investigating the impact of a school reform program on student performance in one particular school.
- 12. 12 QUANTITATIVE RESEARCH • Focuses on numerical data. • Aims to quantify problems and understand patterns. • Uses statistical tools for analysis.
- 13. 13 QUANTITATIVE RESEARCH METHODS CHARACTERISTICS •Uses structured tools like surveys, tests, and questionnaires. •Data is numerical and analyzed statistically.
- 15. 15 Survey Research Purpose: To collect data from a large number of respondents using structured questionnaires or surveys. Focus: Generalizing findings to a larger population based on sample data. Method: Online surveys, phone surveys, or face- to-face interviews with structured questions. Example: A national poll on voter preferences in an upcoming election.
- 16. 16 Correlational Research Purpose: To examine the relationship between two or more variables, without inferring cause and effect. Focus: The strength and direction of relationships between variables. Method: Statistical techniques (e.g., Pearson’s correlation coefficient) to analyze data sets and determine if and how variables are related. Example: Investigating the relationship between hours of study and academic performance among college students.
- 17. 17 Causal-Comparative Research Purpose: To determine the cause or reason for existing differences in a phenomenon by comparing different groups. Focus: Identifying possible causes of differences in variables that already exist. Method: Comparing groups with different levels of a specific variable (e.g., age, gender, exposure to a treatment). Example: Comparing academic performance between students who attended private schools and those who attended public schools to explore potential causes of differences in outcomes.
- 18. 18 Experimental Research Purpose: To determine cause-and-effect relationships between variables by manipulating one or more independent variables and observing the effects on dependent variables. Focus: Establishing causal relationships through controlled experimentation. Method: Randomized controlled trials (RCTs), laboratory experiments, field experiments, with control and experimental groups. Example: Testing the effect of a new teaching method on student performance, where one group uses the new method and another group uses the traditional method.
- 20. 20 True Experimental Design Purpose: To establish causal relationships by manipulating an independent variable (IV) and observing its effect on a dependent variable (DV). Feature: Random assignment of participants to different groups, ensuring that the groups are equivalent before the manipulation.
- 21. 21 Pretest-Posttest Control Group Design: Participants are randomly assigned to either an experimental group or a control group. Both groups are tested before and after the intervention. Example: Testing the effect of a new teaching method on students' performance, where one group receives the new method and the other group continues with the standard method.
- 22. 22 Posttest-Only Control Group Design: Participants are randomly assigned to an experimental group or a control group, but only a posttest is administered after the intervention. Example: Assessing the impact of a drug on blood pressure, where one group receives the drug and the other receives a placebo, and blood pressure is measured after the treatment.
- 23. 23 Solomon Four-Group Design Purpose: To address potential issues like pretest sensitization or test reactivity by combining the pretest- posttest design with a posttest-only design. Feature: Four groups are used — two receive the pretest and the posttest, and two receive only the posttest. This design allows for the examination of the effects of both the intervention and the pretest. Example: Testing the effectiveness of a new teaching method, where two groups take a pretest and posttest, and two groups take only the posttest, to see if the pretest influences the outcomes.
- 25. 25 Nonequivalent Control Group Design Purpose: To compare an experimental group (which receives a treatment or intervention) with a nonrandomly assigned control group (which does not receive the treatment) to assess the effect of the treatment. Feature: Participants are not randomly assigned to the groups, which may introduce pre-existing differences between the groups. Design Structure: Pretest → Intervention → Posttest (for both experimental and control groups) Example: Studying the effect of a new curriculum on student performance in one school (experimental group) compared to a similar school that does not adopt the new curriculum (control group).
- 26. 26 Interrupted Time Series Design Purpose: To examine the effect of an intervention by measuring a dependent variable over an extended period of time before and after the intervention, without random assignment. Feature: Repeated measurements taken at multiple points before and after the intervention (i.e., multiple time points). Design Structure: Multiple pre-intervention observations → Intervention → Multiple post- intervention observations Example: Analyzing the effect of a new law (e.g., banning smoking in public places) on hospital admissions for respiratory illnesses by tracking the data over several months or years before and after the law was enacted.
- 27. 27 Single-Group Interrupted Time Series Design Purpose: A simplified version of the interrupted time series design where data is collected for a single group before and after an intervention, without a separate control group. Feature: Only one group is studied, and the effects are examined by comparing pre- and post-intervention outcomes. Design Structure: Multiple pre-intervention observations → Intervention → Multiple post-intervention observations Example: Studying the effect of a new drug treatment for a disease by measuring health outcomes (e.g., symptom reduction) in patients over time before and after they start the treatment.
- 28. 28 Single-Group Interrupted Time Series Design Purpose: A simplified version of the interrupted time series design where data is collected for a single group before and after an intervention, without a separate control group. Feature: Only one group is studied, and the effects are examined by comparing pre- and post-intervention outcomes. Design Structure: Multiple pre-intervention observations → Intervention → Multiple post-intervention observations Example: Studying the effect of a new drug treatment for a disease by measuring health outcomes (e.g., symptom reduction) in patients over time before and after they start the treatment.
- 29. 29 Cross-Sectional Design with a Comparison Group Purpose: To compare two or more groups at a single point in time, examining the effect of different conditions or interventions. Feature: Groups are not randomly assigned, but a comparison is made between different groups based on their existing conditions (e.g., treatment vs. non-treatment). Design Structure: Collect data on two or more groups at one point in time (e.g., a treatment group and a comparison group). Example: Comparing the academic achievement of students in schools that implemented a new curriculum versus those that did not, based on a cross-sectional survey of student performance at the end of the year.
- 31. 31 THE TWO MOST COMMON TYPES OF MIXED-METHODS DESIGNS • Convergent Parallel Design • Explanatory Sequential Design
- 32. 32 Convergent Parallel Design In this design, both qualitative and quantitative data are collected simultaneously but analyzed separately. After data collection, the results from both methods are compared and interpreted together. The purpose is to compare or validate findings from both types of data to get a more comprehensive understanding of the research problem.
- 33. 33 Explanatory Sequential Design This design involves two phases: the quantitative phase followed by the qualitative phase. The researcher starts by collecting and analyzing quantitative data and then uses qualitative data to further explore or explain the quantitative results.
- 34. FIVE MAJOR PURPOSES OF CONDUCTING MIXED METHODS
- 35. 35 COMPETITIVE LANDSCAPE • Need: • More agility and adaptability • Stronger competitive edge • Ability to adapt swiftly • Stay ahead of the curve • Continuously improve offerings • Integrate user feedback
- 37. 37 E-research, also known as electronic research or digital research, refers to the use of digital technologies and the internet in conducting, managing, and disseminating research. E-research encompasses a wide range of activities that rely on electronic tools and platforms to facilitate various stages of the research process, from data collection and analysis to collaboration and publication.
- 38. TYPES OF E-RESEARCH DESIGN
- 39. 39 Computational and Simulation Research: In fields like physics, biology, and social sciences, e-research often involves the use of computers to run simulations, model complex systems, and analyze computational models. Examples include climate modeling, molecular dynamics simulations, and social network analysis.
- 40. 40 Data-Intensive Research: E-research is crucial for handling and analyzing big data in areas such as genomics, astronomy, economics, and health informatics. These fields rely on digital tools to gather, store, process, and analyze vast datasets that would be difficult to handle without technological support.
- 41. 41 Social Media and Web-Based Research: Social media platforms, web scraping, and sentiment analysis tools allow researchers to study social behavior, public opinion, trends, and communication patterns in the digital world.
- 42. 42 Remote Sensing and Sensor Networks: •E-research includes the use of remote sensing (e.g., satellite imagery) and sensor networks (e.g., environmental sensors, IoT devices) for data collection in fields like environmental science, agriculture, and health.
- 43. 43 Virtual or Augmented Reality (VR/AR) Research: In fields like psychology, education, and health, VR and AR tools are used for experiments, data visualization, and training. These technologies allow researchers to simulate environments or interact with complex data in more immersive ways.
- 45. 45 1. Efficiency 2. Accessibility 3. Collaboration 4. Data Visualization 5. Cost-Effectiveness 6. Reproducibility
- 47. 47 Data Privacy and Security: With the increased reliance on digital platforms, there are growing concerns about the privacy and security of research data, especially with sensitive information (e.g., health data, personal data). Digital Divide: Not all researchers or institutions have equal access to digital tools and high- speed internet, which can create disparities in conducting e-research across different regions or groups.
- 48. 48 Ethical Concerns: The use of digital platforms, particularly for collecting personal data (e.g., social media data), raises ethical issues related to consent, privacy, and data ownership. Quality Control: The vast amount of unfiltered, unverified data available online poses challenges for ensuring the accuracy and quality of data used in e-research.
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